With the increasing demand to reduce emissions and save energy,hydraulic reservoirs require new architecture to optimize their weight,space,and volume.Conventional open reservoirs are large,heavy,and easily polluted,a...With the increasing demand to reduce emissions and save energy,hydraulic reservoirs require new architecture to optimize their weight,space,and volume.Conventional open reservoirs are large,heavy,and easily polluted,and threaten the operation of hydraulic systems.A closed reservoir provides the advantages of small volume and light weight,compared to open reservoirs.In this study,a non-metallic pressure reservoir with variable volume is designed and manufactured for closed-circuit hydraulic systems.The reservoir housing is made of rubber,and the Mooney-Rivlin model is chosen based on the rubber strain properties.The FEA simulation for the reservoir is performed using ANSYS Workbench to obtain the structural stiffness.The major contribution is the establishment of mathematical models for this reservoir,including the volume equation changing with height,flow equation,and force balance equation,to explore the output characteristics of this reservoir.Based on these results,simulation models were built to analyze the output characteristics of the reservoir.Moreover,the test rig of a conventional hydraulic system was transformed into a closed-circuit asymmetric hydraulic system for the reservoir,and preliminary verification experiments were conducted on it.The results demonstrate that the designed reservoir can absorb and discharge oil and supercharge pump inlet to benefit system operation.The changes in the volume and pressure with displacements under different volume ratios and frequencies were obtained,which verified the accuracy of the mathematical models.Owing to its lightweight design and small volume,the reservoir can replace conventional open reservoirs,and this lays a foundation for future theoretical research on this reservoir.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
Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design o...Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples consider the variable stiffness design optimization of typical non-perforated and perforated composite laminates with circular, square, and irregular holes, and systematically discuss the number of candidate discrete fiber laying angles, discrete fiber continuous filtering strategies, and filter radius on structural compliance, continuity, and manufacturability. The optimized discrete fiber angles of variable stiffness laminates are converted into continuous fiber laying paths using a streamlined process for continuous fiber additive manufacturing. Meanwhile, the optimized non-perforated and perforated MBB beams after discrete fiber continuous treatment, are manufactured using continuous fiber co-extrusion additive manufacturing technology to verify the effectiveness of the variable stiffness fiber optimization framework proposed in this paper.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
We consider a single server constant retrial queue,in which a state-dependent service policy is used to control the service rate.Customer arrival follows Poisson process,while service time and retrial time are exponen...We consider a single server constant retrial queue,in which a state-dependent service policy is used to control the service rate.Customer arrival follows Poisson process,while service time and retrial time are exponential distributions.Whenever the server is available,it admits the retrial customers into service based on a first-come first-served rule.The service rate adjusts in real-time based on the retrial queue length.An iterative algorithm is proposed to numerically solve the personal optimal problem in the fully observable scenario.Furthermore,we investigate the impact of parameters on the social optimal threshold.The effectiveness of the results is illustrated by two examples.展开更多
The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach...With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach is proposed to rapidly construct full-scale meso-finite element models for Outer Reduction Yarn Woven Composites(ORYWC)and Inner Reduction Yarn Woven Composites(IRYWC).Then,six independent damage variables are identified:yarn fiber tension/compression,yarn matrix tension/compression,and resin matrix tension/compression.These variables are utilized to establish the constitutive equation of woven composites,considering the coupling effects of microscopic damage.Finally,combined with the Hashin failure criterion and von Mises failure criterion,the strength prediction model is implemented in ANSYS using APDL language to simulate the strength failure process of 2.5DWVTC.The results show that the predicted stiffness and strength values of various parts of ORYWC and IRYWC are in good agreement with the relevant test results.展开更多
The coupled nonlocal nonlinear Schrödinger equations with variable coefficients are researched using the nonstandard Hirota bilinear method.The two-soliton and double-hump one-soliton solutions for the equations ...The coupled nonlocal nonlinear Schrödinger equations with variable coefficients are researched using the nonstandard Hirota bilinear method.The two-soliton and double-hump one-soliton solutions for the equations are first obtained.By assigning different functions to the variable coefficients,we obtain V-shaped,Y-shaped,wave-type,exponential solitons,and so on.Next,we reveal the influence of the real and imaginary parts of the wave numbers on the double-hump structure based on the soliton solutions.Finally,by setting different wave numbers,we can change the distance and transmission direction of the solitons to analyze their dynamic behavior during collisions.This study establishes a theoretical framework for controlling the dynamics of optical fiber in nonlocal nonlinear systems.展开更多
The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achiev...The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
This paper investigates the coupling characteristics of variable mass tank sloshing and ship motion.A full nonlinear numerical model of variable mass tank sloshing-external wave-ship motion coupling is established.Fir...This paper investigates the coupling characteristics of variable mass tank sloshing and ship motion.A full nonlinear numerical model of variable mass tank sloshing-external wave-ship motion coupling is established.Firstly,the coupled motion characteristics of tank sloshing and simplified hull in beam sea are compared,the time history of dimensionless roll angle agrees well with experimental results,and the accuracy of the numerical model is verified.Secondly,the effects of wave excitation and ship speed on the coupled characteristics of Korea Research Institute of Ships and Ocean Engineering very large crude carrier(KVLCC)tank sloshing and ship motion are discussed,and the influence of liquid filling rate on ship heave motion and pitch motion is discussed emphatically.The results show that during the tank filling,the sloshing pressure in the tank increases steadily,and the growth rate is positively correlated with the filling rate.At the same time,the ship pitch motion is less affected by tank sloshing,while the ship heave motion is evidently affected by tank sloshing.展开更多
This work develops a protein imprinted nanosphere with varied recognition specificity for bovine serum albumin(BSA)and lysozyme(Lyz)under different UV light through a gradient dual crosslinked imprinting strategy(i.e....This work develops a protein imprinted nanosphere with varied recognition specificity for bovine serum albumin(BSA)and lysozyme(Lyz)under different UV light through a gradient dual crosslinked imprinting strategy(i.e.,covalent crosslinking and dynamic reversible crosslinking).The imprinting cavities are initially constructed using irreversible covalent crosslinking to specifically recognize BSA,and then the coumarin residues in the imprinting cavities are crosslinked under 365 nm UV light to further imprint Lyz,because Lyz has smaller size than BSA.Since the photo-crosslinking of coumarin is a reversible reaction,the imprinting cavities of Lyz can be de-crosslinked under 254 nm UV light and restore the imprinting cavities of BSA.Moreover,the N-isopropyl acrylamide(NIPAM)and pyrrolidine residues copolymerized in the polymeric surface of the nanospheres are temperature-and p H-responsive respectively.Therefore,the protein rebinding and release behaviors of the nanospheres are controlled by external temperature and p H.As a result,the materials can selectively separate BSA from real bovine whole blood and Lyz from egg white under different UV light.This study may provide a new strategy for construction of protein imprinted materials with tunable specificity for different proteins.展开更多
In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring m...In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources.To address these issues,this study proposes a deep learning-based approach tailored for PSP image deblurring.Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.展开更多
This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorpo...This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorporated into the transport analysis.The Reiner-Rivlin nanofluid model includes variable thermal conductivity,mass diffusivity,and motile microorganism density to accurately reflect realistic biological conditions.Radiative heat transfer and internal heat generation are considered in the thermal energy equation,while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes.The governing equations are non-dimensionalized to reduce complexity,and a numerical solution is obtained using a shooting method.Parametric studies are conducted to examine the influence of key dimensionless parameters on velocity,temperature,concentration,and motile microorganism profiles.The results are presented through a series of graphs,offering insight into the dynamic interplay between physical mechanisms affecting heat and mass transfer in non-Newtonian bioconvective nanofluid systems.展开更多
The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the the...The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the thermal efficiencies of such materials are adversely affected by various thermal features.The purpose of the current work is to demonstrate the thermal analysis of Jeffrey nanofluids with the suspension of microorganisms in the presence of variable thermal sources.The variable effects of thermal conductivity,Brownian diffusivity,and motile density are utilized.The investigated model also reveals the contributions of radiation phenomena and chemical reactions.A porous,saturated,moving surface with a suction phenomenon promotes flow.The modeling of the problem is based on the implementation of the Cattaneo-Christov approach.The convective thermal constraints are used to promote the heat transfer features.A simplified form of the governing model is treated with the assistance of a shooting technique.The physical effects of different parameters for the problem are presented.The current problem justifies its applications in heat transfer,coating processes,heat exchangers,cooling systems in microelectronics,solar systems,chemical processes,etc.展开更多
Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variabl...Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL, is introduced to address the enduring challenge of cross-condition diagnosis in rolling-bearing fault detection. The framework integrates a window global mixed attention mechanism with a deep separable convolutional network, thereby enabling adaptation to fault detection tasks under diverse operating conditions. First, a Convolutional Neural Network (CNN) is employed as the foundational architecture, where the original convolutional layers are enhanced through the incorporation of depthwise separable convolutions, resulting in a Depthwise Separable Convolutional Neural Network (DSCNN) architecture. Subsequently, the extraction of fault characteristics is further refined through a dual-branch network that integrates hybrid attention mechanisms, specifically windowed and global attention mechanisms. This approach enables the acquisition of multi-level feature fusion information, thereby enhancing the accuracy of fault classification. The integration of these features not only optimizes the characteristic extraction process but also yields improvements in accuracy, representational capacity, and robustness in fault feature recognition. In conclusion, the proposed method achieved average precisions of 99.93% and 99.55% in transfer learning tasks, as demonstrated by the experimental results obtained from the CWRU public dataset and the bearing fault detection platform dataset. The experimental findings further provided a detailed comparison between the diagnostic models before and after the enhancement, thereby substantiating the pronounced advantages of the DSCNN-HA-TL approach in accurately identifying faults in critical mechanical components under diverse operating conditions.展开更多
This article analyzes the design of a variable-height simply supported steel truss bridge based on an actual project.It includes its basic situation,introduction to variable-height simply supported steel truss bridges...This article analyzes the design of a variable-height simply supported steel truss bridge based on an actual project.It includes its basic situation,introduction to variable-height simply supported steel truss bridges,key design points of such bridges,and finite element analysis of the design effect.The analysis shows that for such bridges,reasonable main structure design and node design are the keys to determining the overall design idea,and through the reasonable application of the finite element analysis method,the design effect can be scientifically determined,providing a reference for the subsequent structural design of such projects.展开更多
In this paper we present certain bilinear estimates for commutators on Besov spaces with variable smoothness and integrability,and under no vanishing assumptions on the divergence of vector fields.Such commutator esti...In this paper we present certain bilinear estimates for commutators on Besov spaces with variable smoothness and integrability,and under no vanishing assumptions on the divergence of vector fields.Such commutator estimates are motivated by the study of well-posedness results for some models in incompressible fuid mechanics.展开更多
Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplit...Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.展开更多
基金Supported by the National Key Research and Development Program of China(Grant No.2018YFB2000700)National Natural Science Foundation of China(Grant No.51890811).
文摘With the increasing demand to reduce emissions and save energy,hydraulic reservoirs require new architecture to optimize their weight,space,and volume.Conventional open reservoirs are large,heavy,and easily polluted,and threaten the operation of hydraulic systems.A closed reservoir provides the advantages of small volume and light weight,compared to open reservoirs.In this study,a non-metallic pressure reservoir with variable volume is designed and manufactured for closed-circuit hydraulic systems.The reservoir housing is made of rubber,and the Mooney-Rivlin model is chosen based on the rubber strain properties.The FEA simulation for the reservoir is performed using ANSYS Workbench to obtain the structural stiffness.The major contribution is the establishment of mathematical models for this reservoir,including the volume equation changing with height,flow equation,and force balance equation,to explore the output characteristics of this reservoir.Based on these results,simulation models were built to analyze the output characteristics of the reservoir.Moreover,the test rig of a conventional hydraulic system was transformed into a closed-circuit asymmetric hydraulic system for the reservoir,and preliminary verification experiments were conducted on it.The results demonstrate that the designed reservoir can absorb and discharge oil and supercharge pump inlet to benefit system operation.The changes in the volume and pressure with displacements under different volume ratios and frequencies were obtained,which verified the accuracy of the mathematical models.Owing to its lightweight design and small volume,the reservoir can replace conventional open reservoirs,and this lays a foundation for future theoretical research on this reservoir.
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supports for this research were provided by the National Natural Science Foundation of China(No.12272301,12002278,U1906233)the Guangdong Basic and Applied Basic Research Foundation,China(Nos.2023A1515011970,2024A1515010256)+1 种基金the Dalian City Supports Innovation and Entrepreneurship Projects for High-Level Talents,China(2021RD16)the Key R&D Project of CSCEC,China(No.CSCEC-2020-Z-4).
文摘Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples consider the variable stiffness design optimization of typical non-perforated and perforated composite laminates with circular, square, and irregular holes, and systematically discuss the number of candidate discrete fiber laying angles, discrete fiber continuous filtering strategies, and filter radius on structural compliance, continuity, and manufacturability. The optimized discrete fiber angles of variable stiffness laminates are converted into continuous fiber laying paths using a streamlined process for continuous fiber additive manufacturing. Meanwhile, the optimized non-perforated and perforated MBB beams after discrete fiber continuous treatment, are manufactured using continuous fiber co-extrusion additive manufacturing technology to verify the effectiveness of the variable stiffness fiber optimization framework proposed in this paper.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金supported by the National Natural Science Foundation of China(Grant No.11971486)。
文摘We consider a single server constant retrial queue,in which a state-dependent service policy is used to control the service rate.Customer arrival follows Poisson process,while service time and retrial time are exponential distributions.Whenever the server is available,it admits the retrial customers into service based on a first-come first-served rule.The service rate adjusts in real-time based on the retrial queue length.An iterative algorithm is proposed to numerically solve the personal optimal problem in the fully observable scenario.Furthermore,we investigate the impact of parameters on the social optimal threshold.The effectiveness of the results is illustrated by two examples.
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
基金supported by National Science and Technology Major Project,China(No.2017-IV-0007-0044)National Natural Science Foundation of China(No.52175142),National Natural Science Foundation of China(No.52305170)Natural Science Foundation of Sichuan Province,China(No.2022NSFSC1885)。
文摘With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach is proposed to rapidly construct full-scale meso-finite element models for Outer Reduction Yarn Woven Composites(ORYWC)and Inner Reduction Yarn Woven Composites(IRYWC).Then,six independent damage variables are identified:yarn fiber tension/compression,yarn matrix tension/compression,and resin matrix tension/compression.These variables are utilized to establish the constitutive equation of woven composites,considering the coupling effects of microscopic damage.Finally,combined with the Hashin failure criterion and von Mises failure criterion,the strength prediction model is implemented in ANSYS using APDL language to simulate the strength failure process of 2.5DWVTC.The results show that the predicted stiffness and strength values of various parts of ORYWC and IRYWC are in good agreement with the relevant test results.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1604200)the National Natural Science Foundation of China(Grant No.12261131495)Institute of Systems Science,Beijing Wuzi University(Grant No.BWUISS21).
文摘The coupled nonlocal nonlinear Schrödinger equations with variable coefficients are researched using the nonstandard Hirota bilinear method.The two-soliton and double-hump one-soliton solutions for the equations are first obtained.By assigning different functions to the variable coefficients,we obtain V-shaped,Y-shaped,wave-type,exponential solitons,and so on.Next,we reveal the influence of the real and imaginary parts of the wave numbers on the double-hump structure based on the soliton solutions.Finally,by setting different wave numbers,we can change the distance and transmission direction of the solitons to analyze their dynamic behavior during collisions.This study establishes a theoretical framework for controlling the dynamics of optical fiber in nonlocal nonlinear systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.52225212,52272418,U22A20100)National Key Research and Development Program of China(Grant No.2022YFB2503302).
文摘The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024M763646)the China University of Petroleum(East China)Independent Innovation Research Project(Science and Engineering)-Youth Fund(Grant No.24CX06062A)+2 种基金the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation(Grant No.GZC20250181)the Qingdao Natural Science Foundation(Grant No.25-1-1-66-zyyd-jch)Shandong Provincial Key Laboratory of Ocean Engineering(Grant No.kloe202504).
文摘This paper investigates the coupling characteristics of variable mass tank sloshing and ship motion.A full nonlinear numerical model of variable mass tank sloshing-external wave-ship motion coupling is established.Firstly,the coupled motion characteristics of tank sloshing and simplified hull in beam sea are compared,the time history of dimensionless roll angle agrees well with experimental results,and the accuracy of the numerical model is verified.Secondly,the effects of wave excitation and ship speed on the coupled characteristics of Korea Research Institute of Ships and Ocean Engineering very large crude carrier(KVLCC)tank sloshing and ship motion are discussed,and the influence of liquid filling rate on ship heave motion and pitch motion is discussed emphatically.The results show that during the tank filling,the sloshing pressure in the tank increases steadily,and the growth rate is positively correlated with the filling rate.At the same time,the ship pitch motion is less affected by tank sloshing,while the ship heave motion is evidently affected by tank sloshing.
基金financial support from the National Natural Science Foundation of China(No.22275148)National Key R&D Program of China(No.2018YFB1900201)for Qiuyu Zhang+2 种基金the National Natural Science Foundation of China(No.22271232)Fundamental Research Funds for the Central Universities(No.D5000230114)for Shixin Fathe Fundamental Research Funds for the Central Universities(No.D5000220339)for Qing Liu。
文摘This work develops a protein imprinted nanosphere with varied recognition specificity for bovine serum albumin(BSA)and lysozyme(Lyz)under different UV light through a gradient dual crosslinked imprinting strategy(i.e.,covalent crosslinking and dynamic reversible crosslinking).The imprinting cavities are initially constructed using irreversible covalent crosslinking to specifically recognize BSA,and then the coumarin residues in the imprinting cavities are crosslinked under 365 nm UV light to further imprint Lyz,because Lyz has smaller size than BSA.Since the photo-crosslinking of coumarin is a reversible reaction,the imprinting cavities of Lyz can be de-crosslinked under 254 nm UV light and restore the imprinting cavities of BSA.Moreover,the N-isopropyl acrylamide(NIPAM)and pyrrolidine residues copolymerized in the polymeric surface of the nanospheres are temperature-and p H-responsive respectively.Therefore,the protein rebinding and release behaviors of the nanospheres are controlled by external temperature and p H.As a result,the materials can selectively separate BSA from real bovine whole blood and Lyz from egg white under different UV light.This study may provide a new strategy for construction of protein imprinted materials with tunable specificity for different proteins.
基金supported by the National Natural Science Foundation of China(No.12202476).
文摘In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources.To address these issues,this study proposes a deep learning-based approach tailored for PSP image deblurring.Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.
文摘This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorporated into the transport analysis.The Reiner-Rivlin nanofluid model includes variable thermal conductivity,mass diffusivity,and motile microorganism density to accurately reflect realistic biological conditions.Radiative heat transfer and internal heat generation are considered in the thermal energy equation,while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes.The governing equations are non-dimensionalized to reduce complexity,and a numerical solution is obtained using a shooting method.Parametric studies are conducted to examine the influence of key dimensionless parameters on velocity,temperature,concentration,and motile microorganism profiles.The results are presented through a series of graphs,offering insight into the dynamic interplay between physical mechanisms affecting heat and mass transfer in non-Newtonian bioconvective nanofluid systems.
基金appreciation to King Saud University for funding this work through researchers supporting project(No.RSPD2025R1056).
文摘The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the thermal efficiencies of such materials are adversely affected by various thermal features.The purpose of the current work is to demonstrate the thermal analysis of Jeffrey nanofluids with the suspension of microorganisms in the presence of variable thermal sources.The variable effects of thermal conductivity,Brownian diffusivity,and motile density are utilized.The investigated model also reveals the contributions of radiation phenomena and chemical reactions.A porous,saturated,moving surface with a suction phenomenon promotes flow.The modeling of the problem is based on the implementation of the Cattaneo-Christov approach.The convective thermal constraints are used to promote the heat transfer features.A simplified form of the governing model is treated with the assistance of a shooting technique.The physical effects of different parameters for the problem are presented.The current problem justifies its applications in heat transfer,coating processes,heat exchangers,cooling systems in microelectronics,solar systems,chemical processes,etc.
基金supported by the National Natural Science Foundation of China(12272259)the Key Research and Development Fund of Universities in Hebei Province(2510800601A).
文摘Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL, is introduced to address the enduring challenge of cross-condition diagnosis in rolling-bearing fault detection. The framework integrates a window global mixed attention mechanism with a deep separable convolutional network, thereby enabling adaptation to fault detection tasks under diverse operating conditions. First, a Convolutional Neural Network (CNN) is employed as the foundational architecture, where the original convolutional layers are enhanced through the incorporation of depthwise separable convolutions, resulting in a Depthwise Separable Convolutional Neural Network (DSCNN) architecture. Subsequently, the extraction of fault characteristics is further refined through a dual-branch network that integrates hybrid attention mechanisms, specifically windowed and global attention mechanisms. This approach enables the acquisition of multi-level feature fusion information, thereby enhancing the accuracy of fault classification. The integration of these features not only optimizes the characteristic extraction process but also yields improvements in accuracy, representational capacity, and robustness in fault feature recognition. In conclusion, the proposed method achieved average precisions of 99.93% and 99.55% in transfer learning tasks, as demonstrated by the experimental results obtained from the CWRU public dataset and the bearing fault detection platform dataset. The experimental findings further provided a detailed comparison between the diagnostic models before and after the enhancement, thereby substantiating the pronounced advantages of the DSCNN-HA-TL approach in accurately identifying faults in critical mechanical components under diverse operating conditions.
文摘This article analyzes the design of a variable-height simply supported steel truss bridge based on an actual project.It includes its basic situation,introduction to variable-height simply supported steel truss bridges,key design points of such bridges,and finite element analysis of the design effect.The analysis shows that for such bridges,reasonable main structure design and node design are the keys to determining the overall design idea,and through the reasonable application of the finite element analysis method,the design effect can be scientifically determined,providing a reference for the subsequent structural design of such projects.
文摘In this paper we present certain bilinear estimates for commutators on Besov spaces with variable smoothness and integrability,and under no vanishing assumptions on the divergence of vector fields.Such commutator estimates are motivated by the study of well-posedness results for some models in incompressible fuid mechanics.
基金funded by the National Natural Science Foundation of China(grant nos.52475084 and 52375076)the Postdoctoral Fellowship Program of CPSF(grant no.GZC20230202).
文摘Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.
