In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the...To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.展开更多
The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Ti...The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.展开更多
A multi-physics approach was used to quantify the effect of process parameters (laser power, scanning speed, hatch spacing, and scanning strategy) on the thermal history and corresponding microstructure evolution of T...A multi-physics approach was used to quantify the effect of process parameters (laser power, scanning speed, hatch spacing, and scanning strategy) on the thermal history and corresponding microstructure evolution of Ti-25Nb (at%) alloy during the dual-track selective laser melting (SLM) process. Simulation results reveal that during the dual-track SLM process, increasing laser power results in greater thermal accumulation, leading to a molten pool of larger volume and coarser grains. Reducing scanning speed enhances remelting and promotes cellular growth at the top of molten pool, whereas faster scanning speed leads to rougher melt tracks and finer grains. Notably, hatch spacing significantly influences the molten pool dimensions and microstructures, and smaller hatch spacing promotes remelting. Furthermore, the orientations of grains in the second track during zigzag scanning differ markedly from those in the first track. More importantly, compared with those after the first track, both the temperature gradient and cooling rate at the boundaries of remelting molten pool are reduced after the second track scanning, resulting in slower interface velocity and significant change in solidification microstructure. This research provides a theoretical foundation for controlling non-equilibrium microstructure and offering novel insights into the optimization of SLM process parameters of titanium alloys.展开更多
Dear Editors,This letter,reflecting on my research career,is dedi-cated to Professor Qingshi Zhu for his 80th Birthday.Part of this letter is based on my comment“A 20-year journey on the invention of vibrational phot...Dear Editors,This letter,reflecting on my research career,is dedi-cated to Professor Qingshi Zhu for his 80th Birthday.Part of this letter is based on my comment“A 20-year journey on the invention of vibrational photothermal microscopy”published in the May 2025 Nature Meth-ods Focus Issue on Bond-Selective Imaging[1].展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Selective depression of pyrite remains a major bottleneck in copper flotation,particularly when high-pyrite ores are processed and saline water is used.In such environments,conventional approaches using lime and inert...Selective depression of pyrite remains a major bottleneck in copper flotation,particularly when high-pyrite ores are processed and saline water is used.In such environments,conventional approaches using lime and inert grinding media often fail to discriminate ef-fectively between pyrite and valuable copper minerals due to strong copper activation on pyrite surfaces.This study introduced a novel approach using inorganic radicals generated from peroxymonosulfate(PMS)to selectively oxidize and depress pyrite.Flotation tests with synthetic high-pyrite ore blends showed that PMS significantly reduced pyrite recovery while maintaining or improving chalcopyrite flot-ation.Ethylenediaminetetraacetic acid(EDTA)extraction confirmed selective oxidation of pyrite,and electron paramagnetic resonance(EPR)spectroscopy identified hydroxyl(·OH)and sulfate(SO_(4)^(·-))radicals as the dominant reactive species.Iron ions from grinding me-dia and mineral surfaces were identified as key activators of PMS.A major insight was pyrite’s dual role,acting both as a radical scav-enger and an activator,which made it highly reactive and susceptible to radical-induced oxidation.This process converted surface copper-sulfur species into copper hydroxides,effectively suppressing pyrite flotation.While previous studies have applied EPR to detect radicals in simplified activator/precursor systems,this study provides the first direct mechanistic evidence of radical-driven selectivity in flotation by detecting inorganic radicals in a complex flotation slurry,thereby demonstrating their persistence under industrially relevant conditions and establishing a foundation for more effective and targeted flotation strategies.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Selective extraction of precious metals from urban mines plays a crucial role in mitigating the risk of depletion of precious metal resources and reducing waste pollution.However,a major obstacle in precious metal ext...Selective extraction of precious metals from urban mines plays a crucial role in mitigating the risk of depletion of precious metal resources and reducing waste pollution.However,a major obstacle in precious metal extraction lies in the difficulty of distinguishing the subtle differences in the physicochemical characteristics between them,especially gold and palladium.Herein,a proton-driven separation system was presented for cascade recovery of gold and palladium from waste-printed circuit boards(W-PCBs)leachate using poly(amidoxime)(PAO)hydrogel.This exhibits an ultra-high capacity,extra-fast rate,and excellent selectivity for the extraction of Au(Ⅲ)and Pd(Ⅱ).Notably,the separation of Au(Ⅲ)and Pd(Ⅱ)can be achieved with high selectivity at pH=0,resulting in a remarkable separation factor of k_(Au(Ⅲ)/Pd(Ⅱ))=36.5.This was demonstrated to originate from the differential mechanism of PAO hydrogel for the capture of Au(Ⅲ)and Pd(Ⅱ)under proton-mediated conditions.Drawing inspiration from the mechanism,the proton-driven cascade recovery system demonstrates remarkable efficiency in sequentially recovering 99.92%of gold and 99.05%of palladium from W-PCBs acid leachate.This research opens up a strategy to precisely separate and recover precious metals from e-waste of urban mines.展开更多
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
Selective laser melting(SLM)is an advanced additive manufacturing technique that enables the fabrication of complex metal components with high density,precision,and design flexibility.A novel Sc-free Al-4.58Mg-1.17Mn-...Selective laser melting(SLM)is an advanced additive manufacturing technique that enables the fabrication of complex metal components with high density,precision,and design flexibility.A novel Sc-free Al-4.58Mg-1.17Mn-1.59Zr-1.45Ti alloy was successfully fabricated via SLM,achieving a relative density of~99.89%.The microstructure of the as-fabricated alloy was characterized by scanning electron microscopy and transmission electron microscopy,which revealed refined equiaxed grains,a high density of low-angle grain boundaries and dislocation structures,as well as Mg segregation along grain boundaries.Additionally,a variety of dispersed precipitates were identified,including Mg-containing oxides,L1_(2)-Al_(3)(Ti_(x),Zr_(1−x)),and Al_(3)Zr particles.Room-temperature tensile tests showed that the alloy exhibits an excellent combination of strength and ductility,with a yield strength of 453.2±12 MPa,an ultimate tensile strength of 515.1±8 MPa,and an elongation of 22.5%±0.3%.The high strength was attributed to the combined effects of grain boundary strengthening,solid solution strengthening,precipitation strengthening,and dislocation strengthening.The developed Sc-free Al-Mg-Mn-Zr-Ti alloy demonstrates significant potential as an economical high-strength lightweight material for SLM-based manufacturing applications.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their effi...BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.展开更多
The corrosion wear behavior of the selective laser melting(SLM)and forged TC4 alloys in 3.5 wt.%NaCl solution is studied.Results indicate that the current densities of the two TC4 alloys increase with the increase in ...The corrosion wear behavior of the selective laser melting(SLM)and forged TC4 alloys in 3.5 wt.%NaCl solution is studied.Results indicate that the current densities of the two TC4 alloys increase with the increase in applied potential,meaning that the corrosion resistance of the alloys decreases.And the main product of the passive film is TiO_(2).What’s more,corrosion wear behavior is more severe due to the presence of corrosion,resulting in greater mass losses and deeper wear scars.To explore the interaction between corrosion and wear for the two TC4 alloys,the change of the mass loss proportions for wear caused by corrosion and corrosion caused by wear with potential is analyzed.The mass loss of wear caused by corrosion cannot be ignored,and it affects SLM TC4 alloy with the unique acicularα′-phase significantly.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and...As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and nickel(Ni)as sensitive metal cathode materials were evaluated for CO_(2) conversion in MES.The MES with Feelectrode as a promising electrode material demonstrated a superior CO_(2) reduction performance with a maximum acetate accumulation of 417.9±39.2 mg/L,which was 1.5 and 1.7 folds higher than that in the Ni-electrode and Cu-electrode groups,respectively.Furthermore,an outstanding electron recovery efficiency of 67.7%was shown in the Fe-electrode group.The electron transfer between electrode-suspended sludge was systematically cross-evaluated by the electrochemical behavior and extracellular polymeric substances.The Fe-electrode group had the highest electron transfer rate with 0.194 s-1(k_(app)),which was 17.6 and 21.5 times higher than that of the Cu-and Ni-electrode groups,respectively.Fe-electrode was beneficial for reducing electrochemical impedance between the electrode and suspended sludge.Additionally,redox substances in extracellular polymeric substances of the Fe-electrode group were increased,implying more favorable electron transport dynamics.Simultaneously,enrichments of functional bacteria Acetoanerobium and increased key enzymes involved in the carbonyl pathway of the Fe-electrode group were observed,which also promoted CO_(2) conversion in MES.This study provides a perspective on evaluating the promising sensitive metal electrode material for the process of CO_(2) valorization in MES and offers a reference for the subsequent electrode modification.展开更多
Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical...Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical properties under specific conditions.However,the intrinsic influence mechanism of microstructure formation under non-equilibrium solidification conditions in SLM processes has not been clearly revealed.In the present work,the influence of Al concentration and process parameters on the microstructure forming mechanism of Al_(x)CoCrFeNi HEAs prepared by SLM is investigated by molecular dynamics simulation method.The simulation results show that the difference in Al content significantly affects the microstructure formation of HEAs,including the growth rate and morphology of columnar crystals,stress distribution at grain boundaries,and defect structure.In addition,the results show that increasing the substrate temperature improves the solidification formability,reduces microstructural defects,and helps reduce residual stress in Al_(x)CoCrFeNi HEAs.By analyzing the influence of heat and solute flow in the molten pool on the growth of columnar crystals,it is found that spatial fluctuations in Al concentration during the non-equilibrium solidification process inhibit the high cooling rates induced by steep temperature gradients.These findings promote the understanding of the forming mechanism of microstructure in HEAs prepared by SLM and provide theoretical guidance for designing high-performance SLM-fabricated HEAs.展开更多
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金National Natural Science Foundation of China(51504138,51674118,52271177)Hunan Provincial Natural Science Foundation of China(2023JJ50181)Supported by State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology(P2024-022)。
文摘To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.
基金supported by Science and Technology Project of State Grid Corporation Headquarters under Grant 5108-202218280A-2-170-XG(Development and Application of Power Time-Sensitive Network Switching Chip。
文摘The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.
基金Guangdong Basic and Applied Basic Research Foundation (2024A1515011873)Shenzhen Basic Research Project (JCYJ20241202123504007)Shenzhen Science and Technology Innovation Commission (KJZD20240903101400001, KJZD20240903102006009)。
文摘A multi-physics approach was used to quantify the effect of process parameters (laser power, scanning speed, hatch spacing, and scanning strategy) on the thermal history and corresponding microstructure evolution of Ti-25Nb (at%) alloy during the dual-track selective laser melting (SLM) process. Simulation results reveal that during the dual-track SLM process, increasing laser power results in greater thermal accumulation, leading to a molten pool of larger volume and coarser grains. Reducing scanning speed enhances remelting and promotes cellular growth at the top of molten pool, whereas faster scanning speed leads to rougher melt tracks and finer grains. Notably, hatch spacing significantly influences the molten pool dimensions and microstructures, and smaller hatch spacing promotes remelting. Furthermore, the orientations of grains in the second track during zigzag scanning differ markedly from those in the first track. More importantly, compared with those after the first track, both the temperature gradient and cooling rate at the boundaries of remelting molten pool are reduced after the second track scanning, resulting in slower interface velocity and significant change in solidification microstructure. This research provides a theoretical foundation for controlling non-equilibrium microstructure and offering novel insights into the optimization of SLM process parameters of titanium alloys.
文摘Dear Editors,This letter,reflecting on my research career,is dedi-cated to Professor Qingshi Zhu for his 80th Birthday.Part of this letter is based on my comment“A 20-year journey on the invention of vibrational photothermal microscopy”published in the May 2025 Nature Meth-ods Focus Issue on Bond-Selective Imaging[1].
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金support from the Australian Research Council(ARC)Linkage Project(No.LP230100166).
文摘Selective depression of pyrite remains a major bottleneck in copper flotation,particularly when high-pyrite ores are processed and saline water is used.In such environments,conventional approaches using lime and inert grinding media often fail to discriminate ef-fectively between pyrite and valuable copper minerals due to strong copper activation on pyrite surfaces.This study introduced a novel approach using inorganic radicals generated from peroxymonosulfate(PMS)to selectively oxidize and depress pyrite.Flotation tests with synthetic high-pyrite ore blends showed that PMS significantly reduced pyrite recovery while maintaining or improving chalcopyrite flot-ation.Ethylenediaminetetraacetic acid(EDTA)extraction confirmed selective oxidation of pyrite,and electron paramagnetic resonance(EPR)spectroscopy identified hydroxyl(·OH)and sulfate(SO_(4)^(·-))radicals as the dominant reactive species.Iron ions from grinding me-dia and mineral surfaces were identified as key activators of PMS.A major insight was pyrite’s dual role,acting both as a radical scav-enger and an activator,which made it highly reactive and susceptible to radical-induced oxidation.This process converted surface copper-sulfur species into copper hydroxides,effectively suppressing pyrite flotation.While previous studies have applied EPR to detect radicals in simplified activator/precursor systems,this study provides the first direct mechanistic evidence of radical-driven selectivity in flotation by detecting inorganic radicals in a complex flotation slurry,thereby demonstrating their persistence under industrially relevant conditions and establishing a foundation for more effective and targeted flotation strategies.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the National Natural Science Foundation of China grant nos.52470149(P.H.Shao)and 52125002(X.B.Luo)the National Key Research and Development Program of China grant no.2023YFC3905903(P.H.Shao)Nanchang Hangkong University Doctoral Start-up Fund grant no.EA202502100(Y.Y.Zhou).
文摘Selective extraction of precious metals from urban mines plays a crucial role in mitigating the risk of depletion of precious metal resources and reducing waste pollution.However,a major obstacle in precious metal extraction lies in the difficulty of distinguishing the subtle differences in the physicochemical characteristics between them,especially gold and palladium.Herein,a proton-driven separation system was presented for cascade recovery of gold and palladium from waste-printed circuit boards(W-PCBs)leachate using poly(amidoxime)(PAO)hydrogel.This exhibits an ultra-high capacity,extra-fast rate,and excellent selectivity for the extraction of Au(Ⅲ)and Pd(Ⅱ).Notably,the separation of Au(Ⅲ)and Pd(Ⅱ)can be achieved with high selectivity at pH=0,resulting in a remarkable separation factor of k_(Au(Ⅲ)/Pd(Ⅱ))=36.5.This was demonstrated to originate from the differential mechanism of PAO hydrogel for the capture of Au(Ⅲ)and Pd(Ⅱ)under proton-mediated conditions.Drawing inspiration from the mechanism,the proton-driven cascade recovery system demonstrates remarkable efficiency in sequentially recovering 99.92%of gold and 99.05%of palladium from W-PCBs acid leachate.This research opens up a strategy to precisely separate and recover precious metals from e-waste of urban mines.
基金supported by the National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
基金supported by the Jilin Scientific and Technological Development Program(No.20240302108GX)the National Natural Science Foundation of China(Nos.51974032,52174355,51874043,and 51604034).
文摘Selective laser melting(SLM)is an advanced additive manufacturing technique that enables the fabrication of complex metal components with high density,precision,and design flexibility.A novel Sc-free Al-4.58Mg-1.17Mn-1.59Zr-1.45Ti alloy was successfully fabricated via SLM,achieving a relative density of~99.89%.The microstructure of the as-fabricated alloy was characterized by scanning electron microscopy and transmission electron microscopy,which revealed refined equiaxed grains,a high density of low-angle grain boundaries and dislocation structures,as well as Mg segregation along grain boundaries.Additionally,a variety of dispersed precipitates were identified,including Mg-containing oxides,L1_(2)-Al_(3)(Ti_(x),Zr_(1−x)),and Al_(3)Zr particles.Room-temperature tensile tests showed that the alloy exhibits an excellent combination of strength and ductility,with a yield strength of 453.2±12 MPa,an ultimate tensile strength of 515.1±8 MPa,and an elongation of 22.5%±0.3%.The high strength was attributed to the combined effects of grain boundary strengthening,solid solution strengthening,precipitation strengthening,and dislocation strengthening.The developed Sc-free Al-Mg-Mn-Zr-Ti alloy demonstrates significant potential as an economical high-strength lightweight material for SLM-based manufacturing applications.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金Supported by Open Project of Jiangsu Province Key Laboratory of Integrated Traditional Chinese and Western Medicine for the Prevention and Treatment of Geriatric Diseases,No.202232.
文摘BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.
基金supported by the National Natural Science Foundation of China(No.52001142)Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001).
文摘The corrosion wear behavior of the selective laser melting(SLM)and forged TC4 alloys in 3.5 wt.%NaCl solution is studied.Results indicate that the current densities of the two TC4 alloys increase with the increase in applied potential,meaning that the corrosion resistance of the alloys decreases.And the main product of the passive film is TiO_(2).What’s more,corrosion wear behavior is more severe due to the presence of corrosion,resulting in greater mass losses and deeper wear scars.To explore the interaction between corrosion and wear for the two TC4 alloys,the change of the mass loss proportions for wear caused by corrosion and corrosion caused by wear with potential is analyzed.The mass loss of wear caused by corrosion cannot be ignored,and it affects SLM TC4 alloy with the unique acicularα′-phase significantly.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported by the Science and Technology Commission of Shanghai Municipality Foundation(No.22230710500)the Interdisciplinary joint research project of Tongji University(No.2023-3-YB-07).
文摘As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and nickel(Ni)as sensitive metal cathode materials were evaluated for CO_(2) conversion in MES.The MES with Feelectrode as a promising electrode material demonstrated a superior CO_(2) reduction performance with a maximum acetate accumulation of 417.9±39.2 mg/L,which was 1.5 and 1.7 folds higher than that in the Ni-electrode and Cu-electrode groups,respectively.Furthermore,an outstanding electron recovery efficiency of 67.7%was shown in the Fe-electrode group.The electron transfer between electrode-suspended sludge was systematically cross-evaluated by the electrochemical behavior and extracellular polymeric substances.The Fe-electrode group had the highest electron transfer rate with 0.194 s-1(k_(app)),which was 17.6 and 21.5 times higher than that of the Cu-and Ni-electrode groups,respectively.Fe-electrode was beneficial for reducing electrochemical impedance between the electrode and suspended sludge.Additionally,redox substances in extracellular polymeric substances of the Fe-electrode group were increased,implying more favorable electron transport dynamics.Simultaneously,enrichments of functional bacteria Acetoanerobium and increased key enzymes involved in the carbonyl pathway of the Fe-electrode group were observed,which also promoted CO_(2) conversion in MES.This study provides a perspective on evaluating the promising sensitive metal electrode material for the process of CO_(2) valorization in MES and offers a reference for the subsequent electrode modification.
基金supported by the National Natural Science Foundation of China(Grant No.12102133).
文摘Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical properties under specific conditions.However,the intrinsic influence mechanism of microstructure formation under non-equilibrium solidification conditions in SLM processes has not been clearly revealed.In the present work,the influence of Al concentration and process parameters on the microstructure forming mechanism of Al_(x)CoCrFeNi HEAs prepared by SLM is investigated by molecular dynamics simulation method.The simulation results show that the difference in Al content significantly affects the microstructure formation of HEAs,including the growth rate and morphology of columnar crystals,stress distribution at grain boundaries,and defect structure.In addition,the results show that increasing the substrate temperature improves the solidification formability,reduces microstructural defects,and helps reduce residual stress in Al_(x)CoCrFeNi HEAs.By analyzing the influence of heat and solute flow in the molten pool on the growth of columnar crystals,it is found that spatial fluctuations in Al concentration during the non-equilibrium solidification process inhibit the high cooling rates induced by steep temperature gradients.These findings promote the understanding of the forming mechanism of microstructure in HEAs prepared by SLM and provide theoretical guidance for designing high-performance SLM-fabricated HEAs.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
