The current research of direct yaw moment control（DYC） system focus on the design of target yaw moment and the distribution of wheel brake force. The differential braking intervention can effectively improve the lat...The current research of direct yaw moment control（DYC） system focus on the design of target yaw moment and the distribution of wheel brake force. The differential braking intervention can effectively improve the lateral stability of the vehicle, however, the effect of DYC can be improved a step further by applying the control of vehicle longitudinal velocity. In this paper, the relationship between the vehicle longitudinal velocity and lateral stability is studied, and the simulation results show that a decrease of 5 km/h of longitudinal velocity at a particular situation can bring 100° increasing of stable steering upper limit. A critical stable velocity considering the effect of steering and yaw rate measurement is defined to evaluate the risk of losing steer-ability or stability. A novel velocity pre-control method is proposed by using a hierarchical pre-control logic and is integrated with the traditional DYC system. The control algorithm is verified through a hardware in-the-loop simulation system. Double lane change（DLC） test results on both high friction coefficient（μ） and low μ roads show that by using the pre-control method, the steering effort in DLC test can be reduced by 38% and 51% and the peak value of brake pressure control can be reduced by 20% and 12% respectively on high μ and low μ roads, the lateral stability is also improved. This research proposes a novel DYC system with lighter control effort and better control effect.展开更多
In most eukaryotes,oxidative phosphorylation(OXPHOS)is the main energy production process and it involves both mitochondrial and nuclear genomes.The close interaction between the two genomes is critical for the coordi...In most eukaryotes,oxidative phosphorylation(OXPHOS)is the main energy production process and it involves both mitochondrial and nuclear genomes.The close interaction between the two genomes is critical for the coordinated function of the OXPHOS process.Some bivalves show doubly uniparental inheritance(DUI)of mitochondria,where two highly divergent mitochondrial genomes,one inherited through eggs(F-type)and the other through sperm(M-type),coexist in the same individual.However,it remains a puzzle how nuclear OXPHOS genes coordinate with two divergent mitochondrial genomes in DUI species.In this study,we compared transcription,polymorphism,and synonymous codon usage in the mitochondrial and nuclear OXPHOS genes of the DUI species Ruditapes philippinarum using sex-and tissue-specific transcriptomes.Mitochondrial and nuclear OXPHOS genes showed different transcription profiles.Strong co-transcription signal was observed within mitochondrial(separate for F-and M-type)and within nuclear OXPHOS genes but the signal was weak or absent between mitochondrial and nuclear OXPHOS genes,suggesting that the coordination between mitochondrial and nuclear OXPHOS subunits is not achieved transcriptionally.McDonald-Kreitman and frequency-spectrum based tests indicated that M-type OXPHOS genes deviated significantly from neutrality,and that F-type and M-type OXPHOS genes undergo different selection patterns.Codon usage analysis revealed that mutation bias and translational selection were the major factors affecting the codon usage bias in different OXPHOS genes,nevertheless,translational selection in mitochondrial OXPHOS genes appears to be less efficient than nuclear OXPHOS genes.Therefore,we speculate that the coordination between OXPHOS genes may involve post-transcriptional/translational regulation.展开更多
Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overco...Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.展开更多
Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh ...Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.展开更多
Inclusive fitness theory posits that altruistic behaviors,which are directed more likely towards relatives,should be favored by natural selection.However,the prevalence of alternative parenting behaviors in offspring ...Inclusive fitness theory posits that altruistic behaviors,which are directed more likely towards relatives,should be favored by natural selection.However,the prevalence of alternative parenting behaviors in offspring selection,including rejecting their own offspring and accepting the offspring of others,remains poorly understood within the context of parental care evolution.In order to investigate the factors that prompt the occurrence of alternative parenting behaviors,we designed a series of experiments in the Azure-winged Magpie(Cyanopica cyanus).By manipulating the nest spatial position or offspring age/number and parent-offspring familiarity,we addressed how parents provided parental care for the manipulated offspring.In the nest resettlement experiment,the probability of parents rejecting their own offspring significantly increased with nest-moving distances while decreased with offspring ages.In the cross-fostering experiments,the probability of parents provisioning unrelated young significantly decreased with the age difference between cross-fostered chicks.In the nest duplication experiments,where parents were given a choice between familiar offspring and unfamiliar unrelated chicks or between unfamiliar offspring and familiar unrelated chicks,the probability of both alternative parenting behaviors was significantly influenced by the time when parental association with their offspring was deprived.We conclude that as offspring phenotypic traits become individualized and fixed at a special developmental stage,parents gradually acquire the capacity for offspring recognition by associating with them.Any factors that disrupt parent-offspring association or introduce unrelated young into the nest prior to this critical timeline can result in the occurrence of alternative parenting behaviors.展开更多
The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is l...The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is likely to be ineffective if it originates from a poorly ini-tial mesh.Therefore,it is crucial to generate meshes that accurately capture the geometric features.As an indispensable input in meshing methods,the Mesh Size Function(MSF)determines the qual-ity of the generated mesh.However,the current generation of MSF involves human participation tospecify numerous parameters,leading to difficulties in practical usage.Considering the capacity ofmachine learning to reveal the latent relationships within data,this paper proposes a novel machinelearning method,Implicit Geometry Neural Network(IGNN),for automatic prediction of appro-priate MSFs based on the existing mesh data,enabling the generation of unstructured meshes thatalign precisely with geometric features.IGNN employs the generative adversarial theory to learnthe mapping between the implicit representation of the geometry(Signed Distance Function,SDF)and the corresponding MSF.Experimental results show that the proposed method is capableof automatically generating appropriate meshes and achieving comparable meshing results com-pared to traditional methods.This paper demonstrates the possibility of significantly decreasingthe workload of mesh generation using machine learning techniques,and it is expected to increasethe automation level of mesh generation.展开更多
The treatment of chronic wounds presents significant challenges due to the necessity of accelerating healing within complex microenvironments characterized by persistent inflammation and biochemical imbalances.Factors...The treatment of chronic wounds presents significant challenges due to the necessity of accelerating healing within complex microenvironments characterized by persistent inflammation and biochemical imbalances.Factors such as bacterial infections,hyperglycemia,and oxidative stress disrupt cellular functions and impair angiogenesis,substantially delaying wound repair.Nanozymes,which are engineered nanoscale materials with enzyme-like activities,offer distinct advantages over conventional enzymes and traditional nanomaterials,making them promising candidates for chronic wound treatment.To enhance their clinical potential,nanozyme-based catalytic systems are currently being optimized through formulation advancements and preclinical studies assessing their biocompatibility,anti-oxidant activity,antibacterial efficacy,and tissue repair capabilities,ensuring their safety and clinical applicability.When integrated into multifunctional wound dressings,nanozymes modulate reactive oxygen species levels,promote tissue regeneration,and simultaneously combat infections and oxidative damage,extending beyond conventional enzyme-like catalysis in chronic wound treatment.The customizable architectures of nanozymes enable precise therapeutic applications,enhancing their effectiveness in managing complex wound conditions.This review provides a comprehensive analysis of the incorporation of nanozymes into wound dressings,detailing fabrication methods and emphasizing their transformative potential in chronic wound management.By identifying and addressing key limitations,we introduce strategic advancements to drive the development of nanozyme-driven dressings,paving the way for next-generation chronic wound treatments.展开更多
We evaluated the performance of OpenFOAMGPT(GPT for generative pretrained transformers),which includes rating multiple large-language models.Some of the present models efficiently manage different computational fluid ...We evaluated the performance of OpenFOAMGPT(GPT for generative pretrained transformers),which includes rating multiple large-language models.Some of the present models efficiently manage different computational fluid dynamics(CFD)tasks,such as adjusting boundary conditions,turbulence models,and solver configurations,although their token cost and stability vary.Locally deployed smaller models such as the QwQ-32B(Q4 KM quantized model)struggled with generating valid solver files for complex processes.Zero-shot prompts commonly fail in simulations with intricate settings,even for large models.Challenges with boundary conditions and solver keywords stress the need for expert supervision,indicating that further development is needed to fully automate specialized CFD simulations.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51275557,51422505)
文摘The current research of direct yaw moment control（DYC） system focus on the design of target yaw moment and the distribution of wheel brake force. The differential braking intervention can effectively improve the lateral stability of the vehicle, however, the effect of DYC can be improved a step further by applying the control of vehicle longitudinal velocity. In this paper, the relationship between the vehicle longitudinal velocity and lateral stability is studied, and the simulation results show that a decrease of 5 km/h of longitudinal velocity at a particular situation can bring 100° increasing of stable steering upper limit. A critical stable velocity considering the effect of steering and yaw rate measurement is defined to evaluate the risk of losing steer-ability or stability. A novel velocity pre-control method is proposed by using a hierarchical pre-control logic and is integrated with the traditional DYC system. The control algorithm is verified through a hardware in-the-loop simulation system. Double lane change（DLC） test results on both high friction coefficient（μ） and low μ roads show that by using the pre-control method, the steering effort in DLC test can be reduced by 38% and 51% and the peak value of brake pressure control can be reduced by 20% and 12% respectively on high μ and low μ roads, the lateral stability is also improved. This research proposes a novel DYC system with lighter control effort and better control effect.
基金supported by the China Scholarship Council,Italian Ministry of Education University and Research(MIUR)FIR2013 Programme(RBFR13T97A to F.G.)MIUR SIR Programme(RBSI14G0P5 to L.M.)Canziani Bequest to F.G.,and“Ricerca Fondamentale Orientata”(RFO)from the University of Bologna to F.G.and L.M.
文摘In most eukaryotes,oxidative phosphorylation(OXPHOS)is the main energy production process and it involves both mitochondrial and nuclear genomes.The close interaction between the two genomes is critical for the coordinated function of the OXPHOS process.Some bivalves show doubly uniparental inheritance(DUI)of mitochondria,where two highly divergent mitochondrial genomes,one inherited through eggs(F-type)and the other through sperm(M-type),coexist in the same individual.However,it remains a puzzle how nuclear OXPHOS genes coordinate with two divergent mitochondrial genomes in DUI species.In this study,we compared transcription,polymorphism,and synonymous codon usage in the mitochondrial and nuclear OXPHOS genes of the DUI species Ruditapes philippinarum using sex-and tissue-specific transcriptomes.Mitochondrial and nuclear OXPHOS genes showed different transcription profiles.Strong co-transcription signal was observed within mitochondrial(separate for F-and M-type)and within nuclear OXPHOS genes but the signal was weak or absent between mitochondrial and nuclear OXPHOS genes,suggesting that the coordination between mitochondrial and nuclear OXPHOS subunits is not achieved transcriptionally.McDonald-Kreitman and frequency-spectrum based tests indicated that M-type OXPHOS genes deviated significantly from neutrality,and that F-type and M-type OXPHOS genes undergo different selection patterns.Codon usage analysis revealed that mutation bias and translational selection were the major factors affecting the codon usage bias in different OXPHOS genes,nevertheless,translational selection in mitochondrial OXPHOS genes appears to be less efficient than nuclear OXPHOS genes.Therefore,we speculate that the coordination between OXPHOS genes may involve post-transcriptional/translational regulation.
基金financially supported in-part by the National Natural Science Foundation of China(52275011)the Natural Science Foundation of Guangdong Province(2023B1515020080)+3 种基金the Natural Science Foundation of Guangzhou(2024A04J2552)the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253)the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).
文摘Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.
基金provided by the National Natural Sciences Foundation of China (Grant 32071491,31772465,31672299,31572271,and 32260128)。
文摘Inclusive fitness theory posits that altruistic behaviors,which are directed more likely towards relatives,should be favored by natural selection.However,the prevalence of alternative parenting behaviors in offspring selection,including rejecting their own offspring and accepting the offspring of others,remains poorly understood within the context of parental care evolution.In order to investigate the factors that prompt the occurrence of alternative parenting behaviors,we designed a series of experiments in the Azure-winged Magpie(Cyanopica cyanus).By manipulating the nest spatial position or offspring age/number and parent-offspring familiarity,we addressed how parents provided parental care for the manipulated offspring.In the nest resettlement experiment,the probability of parents rejecting their own offspring significantly increased with nest-moving distances while decreased with offspring ages.In the cross-fostering experiments,the probability of parents provisioning unrelated young significantly decreased with the age difference between cross-fostered chicks.In the nest duplication experiments,where parents were given a choice between familiar offspring and unfamiliar unrelated chicks or between unfamiliar offspring and familiar unrelated chicks,the probability of both alternative parenting behaviors was significantly influenced by the time when parental association with their offspring was deprived.We conclude that as offspring phenotypic traits become individualized and fixed at a special developmental stage,parents gradually acquire the capacity for offspring recognition by associating with them.Any factors that disrupt parent-offspring association or introduce unrelated young into the nest prior to this critical timeline can result in the occurrence of alternative parenting behaviors.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is likely to be ineffective if it originates from a poorly ini-tial mesh.Therefore,it is crucial to generate meshes that accurately capture the geometric features.As an indispensable input in meshing methods,the Mesh Size Function(MSF)determines the qual-ity of the generated mesh.However,the current generation of MSF involves human participation tospecify numerous parameters,leading to difficulties in practical usage.Considering the capacity ofmachine learning to reveal the latent relationships within data,this paper proposes a novel machinelearning method,Implicit Geometry Neural Network(IGNN),for automatic prediction of appro-priate MSFs based on the existing mesh data,enabling the generation of unstructured meshes thatalign precisely with geometric features.IGNN employs the generative adversarial theory to learnthe mapping between the implicit representation of the geometry(Signed Distance Function,SDF)and the corresponding MSF.Experimental results show that the proposed method is capableof automatically generating appropriate meshes and achieving comparable meshing results com-pared to traditional methods.This paper demonstrates the possibility of significantly decreasingthe workload of mesh generation using machine learning techniques,and it is expected to increasethe automation level of mesh generation.
基金supported by the Key Project of the Joint Fund for Regional Innovation and Development of the National Natural Science Foundation of China(U23A20686)the National Natural Science Foundation of China(81901979)+2 种基金the Peking University People’s Hospital Scientific Research Development Funds(RDJP2022-07)the Joint Funds for the Innovation of Science and Technology,Fujian Province(2023Y9226)the Introduced High-Level Talent Team Project of Quanzhou City(2023CT008).
文摘The treatment of chronic wounds presents significant challenges due to the necessity of accelerating healing within complex microenvironments characterized by persistent inflammation and biochemical imbalances.Factors such as bacterial infections,hyperglycemia,and oxidative stress disrupt cellular functions and impair angiogenesis,substantially delaying wound repair.Nanozymes,which are engineered nanoscale materials with enzyme-like activities,offer distinct advantages over conventional enzymes and traditional nanomaterials,making them promising candidates for chronic wound treatment.To enhance their clinical potential,nanozyme-based catalytic systems are currently being optimized through formulation advancements and preclinical studies assessing their biocompatibility,anti-oxidant activity,antibacterial efficacy,and tissue repair capabilities,ensuring their safety and clinical applicability.When integrated into multifunctional wound dressings,nanozymes modulate reactive oxygen species levels,promote tissue regeneration,and simultaneously combat infections and oxidative damage,extending beyond conventional enzyme-like catalysis in chronic wound treatment.The customizable architectures of nanozymes enable precise therapeutic applications,enhancing their effectiveness in managing complex wound conditions.This review provides a comprehensive analysis of the incorporation of nanozymes into wound dressings,detailing fabrication methods and emphasizing their transformative potential in chronic wound management.By identifying and addressing key limitations,we introduce strategic advancements to drive the development of nanozyme-driven dressings,paving the way for next-generation chronic wound treatments.
基金supported by the Royal Society(Grant No.RG\R1\251236)the Fundamental Research Funds for the Central Universities of China(Grant No.JKF-2025055317102).
文摘We evaluated the performance of OpenFOAMGPT(GPT for generative pretrained transformers),which includes rating multiple large-language models.Some of the present models efficiently manage different computational fluid dynamics(CFD)tasks,such as adjusting boundary conditions,turbulence models,and solver configurations,although their token cost and stability vary.Locally deployed smaller models such as the QwQ-32B(Q4 KM quantized model)struggled with generating valid solver files for complex processes.Zero-shot prompts commonly fail in simulations with intricate settings,even for large models.Challenges with boundary conditions and solver keywords stress the need for expert supervision,indicating that further development is needed to fully automate specialized CFD simulations.
