Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.展开更多
The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimiz...The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimizing their design.However,conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs.This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks(PINNs)with Finite Element Method(FEM)derived data for the robust analysis of thick-walled,axisymmetric,heterogeneous,hyperelastic pressure vessels with elliptical geometries.A PINN framework incorporating neo-Hookean constitutive relations is developed in MATLAB.To enhance training efficiency and accuracy,the PINN’s loss function is augmented with displacement data obtained from high-fidelity FEM simulations performed in ANSYS.The methodology is rigorously validated by comparing PINN-predicted displacement and von Mises stress fields against ANSYS benchmarks for various scenarios of FGMconfigurations(with material properties varying according to a power law)subjected to internal and external pressurization.The results demonstrate excellent agreement between the proposed hybrid PINN-FEMapproach and conventional FEMsolutions across all test cases,accurately capturing complex deformation patterns and stress concentrations.This study highlights the potential of data-augmented PINNs as an effective and accurate computational tool for tackling complex solid mechanics problems involving non-linearmaterials and significant heterogeneity,offering a promising avenue for future research in engineering design and analysis.展开更多
In this paper, we introduce some new subclasses of meromorphically uniformly reciprocal starlike functions associated with the generalized Dziok-Srivastava operator and its corresponding integral operator defined by s...In this paper, we introduce some new subclasses of meromorphically uniformly reciprocal starlike functions associated with the generalized Dziok-Srivastava operator and its corresponding integral operator defined by subordination. We obtain the inclusion relation, sufficient conditions and raajorization property of the class. Moreover, we point out some new and interesting corollaries of our main result. These results generalize some known results.展开更多
Objective:To evaluate the effect of task-oriented training combined with a lower limb rehabilitation robot on improving motor function and ankle joint function in stroke patients with hemiplegia.Methods:Sixty-three st...Objective:To evaluate the effect of task-oriented training combined with a lower limb rehabilitation robot on improving motor function and ankle joint function in stroke patients with hemiplegia.Methods:Sixty-three stroke patients with hemiplegia admitted to our hospital from January 2022 to June 2024 were randomly divided into observation group(32 cases)and control group(31 cases)using the envelope method.The control group received task-oriented training,while the observation group received additional lower limb rehabilitation robot training.The motor function(Fugl-Meyer Assessment of Lower Extremity,FMA-LE)and ankle joint function(Active Dorsiflexion Range of Motion,DF AROM)were compared between the two groups.Results:After treatment,the levels of FMA-LE and DF AROM in both groups increased significantly,and the improvement in each index in the observation group was better than that in the control group(P<0.05).Conclusion:The combination of task-oriented training and lower limb rehabilitation robot training can more effectively improve the overall motor function of the lower limbs and the active dorsiflexion ability of the ankle joint in stroke patients with hemiplegia.展开更多
A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synth...A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.展开更多
This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared ...This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the bi...Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the biosynthetic pathway of saikosaponins,we identified two cytochrome P450 monooxygenases,CYP716A41 and CYP716Y4,in Bupleurum chinense.These enzymes catalyze the C-28 oxidation and C-16 hydroxylation of oleanane-type triterpene skeletons,respectively.The catalytic efficiency of CYP716A41 from a southern B.chinense variety was significantly higher than that from a northern variety.Molecular docking and mutagenesis experiments revealed that amino acid residues at sites 9 and 35 may contribute to this difference in catalytic efficiency.Additionally,under cold stress,the expression levels of both CYP450 genes and the saikosaponin contents in the leaves of southern varieties were significantly higher compared to those in northern varieties.The variation in the catalytic efficiency of CYP716A41 and the differential expression of the two CYP450 genes under cold stress during winter are associated with the differences in saikosaponin biosynthesis in the leaves of southern and northern B.chinense varieties.This is consistent with the distinct medicinal usage practices observed between southern and northern China.展开更多
The generalized Dziok-Srivastava operator is used here to introduce a class of analytic functions in the open unit disc. We provide convolution properties, some subordination relations and the problem of radius. The r...The generalized Dziok-Srivastava operator is used here to introduce a class of analytic functions in the open unit disc. We provide convolution properties, some subordination relations and the problem of radius. The results presented here extend some of the earlier results.展开更多
By using the hypergeometric function defined by the Dziok-Srivastava operator, a new subclass of meromorphic function is introdued. We obtain Fekete-Szeg? inequalities for the meromorphic function f(z) for which α-(1...By using the hypergeometric function defined by the Dziok-Srivastava operator, a new subclass of meromorphic function is introdued. We obtain Fekete-Szeg? inequalities for the meromorphic function f(z) for which α-(1 + α{1 +z[_lI_mf(z)]′′/[_lI_mf(z)]′}/z[_lI_mf(z)]′/_lI_mf(z))■φ(z)(α ∈ C-{1/2, 1}).展开更多
Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in s...Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.展开更多
This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breac...This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
文摘The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimizing their design.However,conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs.This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks(PINNs)with Finite Element Method(FEM)derived data for the robust analysis of thick-walled,axisymmetric,heterogeneous,hyperelastic pressure vessels with elliptical geometries.A PINN framework incorporating neo-Hookean constitutive relations is developed in MATLAB.To enhance training efficiency and accuracy,the PINN’s loss function is augmented with displacement data obtained from high-fidelity FEM simulations performed in ANSYS.The methodology is rigorously validated by comparing PINN-predicted displacement and von Mises stress fields against ANSYS benchmarks for various scenarios of FGMconfigurations(with material properties varying according to a power law)subjected to internal and external pressurization.The results demonstrate excellent agreement between the proposed hybrid PINN-FEMapproach and conventional FEMsolutions across all test cases,accurately capturing complex deformation patterns and stress concentrations.This study highlights the potential of data-augmented PINNs as an effective and accurate computational tool for tackling complex solid mechanics problems involving non-linearmaterials and significant heterogeneity,offering a promising avenue for future research in engineering design and analysis.
基金Supported by the National Natural Science Foundation of China(11561001)Supported by the Natural Science Foundation of Inner Mongolia Province(2014MS0101)Supported by the Higher School Foundation of Inner Mongolia Province(2015NJZY240)
文摘In this paper, we introduce some new subclasses of meromorphically uniformly reciprocal starlike functions associated with the generalized Dziok-Srivastava operator and its corresponding integral operator defined by subordination. We obtain the inclusion relation, sufficient conditions and raajorization property of the class. Moreover, we point out some new and interesting corollaries of our main result. These results generalize some known results.
文摘Objective:To evaluate the effect of task-oriented training combined with a lower limb rehabilitation robot on improving motor function and ankle joint function in stroke patients with hemiplegia.Methods:Sixty-three stroke patients with hemiplegia admitted to our hospital from January 2022 to June 2024 were randomly divided into observation group(32 cases)and control group(31 cases)using the envelope method.The control group received task-oriented training,while the observation group received additional lower limb rehabilitation robot training.The motor function(Fugl-Meyer Assessment of Lower Extremity,FMA-LE)and ankle joint function(Active Dorsiflexion Range of Motion,DF AROM)were compared between the two groups.Results:After treatment,the levels of FMA-LE and DF AROM in both groups increased significantly,and the improvement in each index in the observation group was better than that in the control group(P<0.05).Conclusion:The combination of task-oriented training and lower limb rehabilitation robot training can more effectively improve the overall motor function of the lower limbs and the active dorsiflexion ability of the ankle joint in stroke patients with hemiplegia.
基金supported by grants from the Guangxi Science and Technology Major Project(GKAA24206023)the Biological Breeding-National Science and Technology Major Project(2024ZD04077)+2 种基金the National Natural Science Foundation of China(32272120)the National Key Research and Development Program of China(2024YFF1000800)the Guangdong Basic Research Center of Excellence for Precise Breeding of Future Crops Major Project(FCBRCE-202502,FCBRCE-202504).
文摘A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.
基金supported by the National Natural Science Foundation of China (No.82174074)。
文摘This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
基金supported by CARS(CARS-21),the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-032)the Science and Technology Department of Xizang(XZ202401ZY0020)+2 种基金the Science and Technology Department of Sichuan Province(2023YFH0044,2023YFH0018)the Sichuan Province Science Foundation for Distinguished Young Scholars(2022JDJQ0006)the Doctoral Fund of Southwest University of Science and Technology(19ZX7117,21ZX7116).
文摘Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the biosynthetic pathway of saikosaponins,we identified two cytochrome P450 monooxygenases,CYP716A41 and CYP716Y4,in Bupleurum chinense.These enzymes catalyze the C-28 oxidation and C-16 hydroxylation of oleanane-type triterpene skeletons,respectively.The catalytic efficiency of CYP716A41 from a southern B.chinense variety was significantly higher than that from a northern variety.Molecular docking and mutagenesis experiments revealed that amino acid residues at sites 9 and 35 may contribute to this difference in catalytic efficiency.Additionally,under cold stress,the expression levels of both CYP450 genes and the saikosaponin contents in the leaves of southern varieties were significantly higher compared to those in northern varieties.The variation in the catalytic efficiency of CYP716A41 and the differential expression of the two CYP450 genes under cold stress during winter are associated with the differences in saikosaponin biosynthesis in the leaves of southern and northern B.chinense varieties.This is consistent with the distinct medicinal usage practices observed between southern and northern China.
基金Supported by the Natural Science Foundation of Inner Mongolia of China(2014MS0101)Supported by the Natural Science Foundation of China(11561001)Supported by the Higher School Research Foundation of Inner Mongolia of China(NJZY16251)
文摘The generalized Dziok-Srivastava operator is used here to introduce a class of analytic functions in the open unit disc. We provide convolution properties, some subordination relations and the problem of radius. The results presented here extend some of the earlier results.
基金The NSF(KJ2015A372)of Anhui Provincial Department of Education
文摘By using the hypergeometric function defined by the Dziok-Srivastava operator, a new subclass of meromorphic function is introdued. We obtain Fekete-Szeg? inequalities for the meromorphic function f(z) for which α-(1 + α{1 +z[_lI_mf(z)]′′/[_lI_mf(z)]′}/z[_lI_mf(z)]′/_lI_mf(z))■φ(z)(α ∈ C-{1/2, 1}).
基金supported by the National Natural Science Foundation of China,Nos.82072165 and 82272256(both to XM)the Key Project of Xiangyang Central Hospital,No.2023YZ03(to RM)。
文摘Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.
基金supported by National Key R&D Program of China:Gravitational Wave Detection Project(Grant Nos.2021YFC22026,2021YFC2202601,2021YFC2202603)National Natural Science Foundation of China(Grant Nos.12172288 and 12472046)。
文摘This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.
