This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the id...This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the idea Of the algorithm is applied to probability computing.展开更多
The safety evaluation of engineering systems whose performance evaluation requires finite element analysis is a challenge in reliability theory.Recently,Adjusted Control Variates Technique(ACVAT)has proposed by the au...The safety evaluation of engineering systems whose performance evaluation requires finite element analysis is a challenge in reliability theory.Recently,Adjusted Control Variates Technique(ACVAT)has proposed by the authors to solve this issue.ACVAT uses the results of a finite element method(FEM)model with coarse mesh density as the control variates of the model with fine mesh and efficiently solves FEM-based reliability problems.ACVAT however does not provide any results about the reliability-based mesh convergence of the problem,which is an important tool in FEM.Mesh-refinement analysis allows checking whether the numerical solution is sufficiently accurate,even though the exact solution is unknown.In this study,by introducing expanded control variates(ECV)formulation,ACVAT is improved and the capabilities of the method are also extended for efficient reliability mesh convergence analysis ofFEM-based reliability problems.In the present study,the FEM-based reliability analyses of four practical engineering problems are investigated by this method and the corresponding results are compared with accurate results obtained by analytical solutions for two problems.The results confirm that the proposed approach not only handles the mesh refinement progress with the required accuracy,but it also reduces considerably the computational cost of FEM-based reliability problems.展开更多
In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solu...In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solution sequence with negative energy for the system via a variant Clark’s theorem.Then we get the asymptotical property of the solution sequence by L∞norm.展开更多
Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th...Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.展开更多
Plastome variation,including single spontaneous nucleotide substitutions and single insertions/deletions,is the major source of leaf variegation in plants.Additionally,one recent study has showed that a simple plastom...Plastome variation,including single spontaneous nucleotide substitutions and single insertions/deletions,is the major source of leaf variegation in plants.Additionally,one recent study has showed that a simple plastome structural variation,which is induced by one pair of small inverted repeats,can also result in leaf variegation.Here we show a complex plastome structural variation caused by intermolecular and intramolecular recombination across three pairs of small inverted repeats accounts for leaf variegation in a widely cultivated shrub Heptapleurum ellipticum(Araliaceae).This plastome structural variation contains two deletions and two duplications,resulting in dramatic expansion of IRs,substantial contraction of LSC and loss of 11 genes that essential for photosynthesis.Plastome heteroplasmy was detected in both green and albino sectors of variegated leaves.Relative to green sectors,albino sectors in the variegated leaves exhibit significantly reduced expression for the 11 genes lost in the mutated plastome as well as 26 other genes,but significantly increased expression for one gene related to translation apparatus.Optical and transmission electron microscopy observations showed that mesophyll cells of albino sectors possess plastids lacking grana lamellae,which likely carry the mutated plastome and contribute to albinism.In both sectors,the first layer of spongy mesophyll cells beneath the lower epidermis contains normal chloroplasts,suggesting periclinal division of the lower epidermis during development.Our study demonstrates that multiple small repeats can collectively mediate intra-and inter-molecular recombination in plastome and offers a new mechanism accounting for leaf variegation in plants.展开更多
Carbonyls play an important role in the atmospheric chemistry as the main precursor for reactive radicals and peroxyacetyl nitrate.However,little research has been conducted so far on the seasonal variations of carbon...Carbonyls play an important role in the atmospheric chemistry as the main precursor for reactive radicals and peroxyacetyl nitrate.However,little research has been conducted so far on the seasonal variations of carbonyls in China’s urban atmosphere.In this study,ambient carbonyls were 24-hourly observed in four seasons in Hangzhou,a mega-city in the Yangtze River Delta Region,China.The concentration of total carbonyls was the highest in summer(44.35μg/m^(3)),the second in winter(44.05μg/m^(3)),the third in spring(29.31μg/m^(3))and autumn(27.11μg/m^(3)).The most abundant species were found to be acetone in spring,summer,and winter,while formaldehyde in autumn.Rainfall can significantly reduce the concentrations of most ambient carbonyls,with the largest decrease observed in the wet precipitation events occurring in spring and summer,while acetone concentrations remained invariable due to its lower water solubility.Multiple linear regression analysis and carbonyls ratios indicated that anthropogenic emissions were the predominant sources of carbonyls,and atmospheric formaldehyde was mainly emitted from primary sources other than secondary sources.Vehicular exhaust was identified as the primary source of ambient carbonyls,particularly in winter,and its contribution reached 92.80%to formaldehyde.Additionally,photochemical reactions were closely associated with the secondary production of formaldehyde in summer.Carbonyls showed strong ozone formation potential in all four seasons.Based on the health risk assessment,the exposure to ambient carbonyls is harmful to outdoor pedestrians.The results could provide essential information and references for simulating regional air quality and analyzing ozone pollution,which is essential for improving air quality in the Yangtze River Delta region.展开更多
Structural variation is an important source of genetic variation in wheat and have been important in the evolution of the wheat's genome.Few studies have examined the relationship between structural variations and...Structural variation is an important source of genetic variation in wheat and have been important in the evolution of the wheat's genome.Few studies have examined the relationship between structural variations and agronomy and drought tolerance.The present study identified structural chromosome variations(SCVs)in a doubled haploid(DH)population and backcross introgression lines(BC5F3)derived from Jinmai 47 and Jinmai 84 using fluorescence in situ hybridization(FISH).There are one simple translocation,10 present/absent variations(PAVs),and one copy number variation(CNV)between Jinmai 47 and Jinmai 84,which distributed in 10 chromosomes.Eight SCVs were associated with 15 agronomic traits.A PAV recombination occurred on chromosome 2A,which was associated with grain number per spike(GNS).The 1BL/1RS translocation and PAV.2D were associated with significant reductions in plant height,deriving from the effects on LI2-LI4,LI2-LI4 and UI,respectively respectively.PAV.2D was also contributed to an increase of 3.13%for GNS,1BL/1RS significantly increased spikelet number,grain length(GL),and grain thickness(GT).The effect of PAV.4A.1 on GL,PAV.6A on spike length(SL)and thousand-grain weight(TGW),PAV.6B on SL,GT and TGW were identified and verified.PAVs on chromosomes 2A,6A,1D,2D,and a CNV on chromosome 4B were associated with the drought tolerance coefficients.Additive and interaction effects among SCVs were observed.Many previously cloned key genes and yield-related QTL were found in polymorphic regions of PAV.2B,PAV.2D,and CNV.4B.Altogether,this study confirmed the genetic effect of SCVs on agronomy and drought tolerance,and identification of these SCVs will facilitate genetic improvement of wheat through marker-assisted selection.展开更多
Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subje...Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.展开更多
Xishui National Forest Park in Heilongjiang Province hosts China's most pristine temperate forests and serves as a key site for ecotourism and forest therapy.However,the emission patterns of phytoncides(key bio ac...Xishui National Forest Park in Heilongjiang Province hosts China's most pristine temperate forests and serves as a key site for ecotourism and forest therapy.However,the emission patterns of phytoncides(key bio active compounds) remain poorly understood,limiting their therapeutic application.This study provides the first comprehensive characterization of spatiotemporal dynamics in airborne phytoncides and their synergistic interactions with environmental factors throughout the autumn-early spring seasonal transition in a temperate forest ecosystem.We analyzed the compositional dynamics of phytoncides and terpenoid content variations using thermal desorption-gas chromatography-mass spectrometry(TD-GC-MS) from September 2024 to March 2025.This period encompassed seasonal transitions from autumn to early spring,including diurnal variations in September and snowfall events in November.The method demonstrated detection limits(LODs) ranging from 1.35 to 5.33 ng m-3 and quantification limits(LOQs) from 4.09 to 16.15 ng m-3.Our results revealed pronounced seasonal fluctuations in phytoncide composition.In September,terpenoids,esters,alcohols,and alkanes displayed a diurnal "decrease-increase" trend,whereas aldehydes and ketones peaked at midday.Notably,esters and alcohols were undetectable in November and January.By January,terpenoids reached their lowest proportion(0.17±0.02%) at noon.Five terpenoids(α-pinene,myrcene,D-limonene,camphene,p-cymene) were detected in September,four(α-pinene,D-limonene,camphene,p-cymene) in November,two(D-limonene,p-cymene) in January,and only p-cymene in March.The total concentration and emission rate of the five terpenoids peaked in September afternoons at 1961.58±106.67 ng m^(-3) and653.86±35.56 ng m^(-3) h^(-1),respectively.Nocturnal emissions(32131.95±2522.21 ng m^(-3)) significantly surpassed daytime levels(14473.04±958.49 ng m^(-3)),with emission rates escalating from 1447.30±95.85 ng m^(-3) h^(-1)(day) to 5355.33±420.37 ng m^(-3) h^(-1)(night),marking a3.7-fold increase.Snowfall dramatically elevated terpenoid concentrations(pre-snowfall:158.58±14.12 ng m^(-3);post-snowfall:1080.57±57.76 ng m^(-3)) and emission rates(pre-snowfall:52.86±4.71 ng m^(-3) h^(-1);post-snowfall:360.19±19.25 ng m^(-3) h^(-1)),reflecting a 6.8-fold surge.This study underscores the profound influence of light intensity,seasonal shifts,and climatic conditions on airborne phytoncide levels,offering a scientific foundation for optimizing forest therapy and ecotourism strategies.展开更多
Finger millet(Eleusine coracana Gaertn.),a nutritionally rich and drought-resilient C4 cereal,possesses exceptional grain storage longevity(up to 50 years).Here,we report a high-quality genome assembly of the allotetr...Finger millet(Eleusine coracana Gaertn.),a nutritionally rich and drought-resilient C4 cereal,possesses exceptional grain storage longevity(up to 50 years).Here,we report a high-quality genome assembly of the allotetraploid cultivar C142,revealing extensive structural rearrangements between its two subgenomes(subA and subB),which are associated with asymmetric gene expression and subgenome dominance favoring subA.SubB diverged from subA and E.indica approximately 6.8 million years ago.Subsequently,two whole-genome duplication events shaped the current genome architecture,contributing to gene redundancy and adaptive potential.Notably,expansion of stress-related gene families,such as aldo-keto reductases,suggests a role in oxidative stress response and drought adaptation.Using genome-wide association studies,we identify several candidate genes associated with key agronomic traits.Among them,EcMDHAR,encoding monodehydroascorbate reductase,plays a critical role in enhancing drought tolerance.Different EcMDHAR haplotypes exhibit distinct expression profiles,supporting their functional relevance in drought adaptation.This genomic resource not only advances our understanding of polyploid genome evolution in millets,but also provides a foundation for genome-assisted improvement of drought resistance and nutritional quality in finger millet.展开更多
Cynomolgus macaques,a species of Old World primate native to southeastern and eastern Asia and the island of Mauritius,are one of the most important nonhuman primate models for infectious disease.Although the closely ...Cynomolgus macaques,a species of Old World primate native to southeastern and eastern Asia and the island of Mauritius,are one of the most important nonhuman primate models for infectious disease.Although the closely related rhesus macaque is classified into subspecies based on geographic origin,no such subdivision exists for cynomolgus macaques,and they continue to be used interchangeably in infectious disease research,reducing the comparability of data produced from these studies.Research into the population genetics of cynomolgus macaques has found significant differences between macaques native to different areas,including their genetic diversity,with macaques from insular populations such as Mauritius and the Philippines exhibiting highly restricted heterozygosity compared to mainland populations native to Indonesia or Cambodia.In the context of infectious disease studies,research into pathogens,including Ebola virus,Crimean-Congo hemorrhagic fever virus,and Mycobacterium tuberculosis have found differences in study outcomes,survival times,and immune cell responses between different populations of macaques.This review provides an overview of the differences between cynomolgus macaque populations in the context of genetic diversity,and in response to infection,and highlights the need for clear reporting of geographic origin of primates used in research.This will improve data comparison between studies and help to further refine this important animal model.展开更多
Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial het...Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.展开更多
Signal transduction in a cell is mostly mediated with biochemical reactions which are noisy and often modeled with chemical master equations or Langevin type of dynamics.Thus stochastic simulation constitutes a major ...Signal transduction in a cell is mostly mediated with biochemical reactions which are noisy and often modeled with chemical master equations or Langevin type of dynamics.Thus stochastic simulation constitutes a major part of computation in cell signaling.Nevertheless,the presence of a wide span of time scales or molecular numbers in various pathways may lead to trouble in computation efficiency or accuracy.To avoid this problem,the commonly employed variational method evolves the whole probability distribution and reduces the stochastic equations to deterministic ones of only a few parameters.However,the design of the left variational basis is essential for its successful application,especially to large networks.In this paper,we extend the conventional polynomial basis to the Fourier and further the Gaussian basis,much facilitating description of multi-peaked or localized non-Gaussian distributions and at the same time avoiding numerical instability and computational complexity frequently encountered with conventional basis.The extension here is demonstrated in several typical biochemical signaling networks and achieves similar accuracy as the benchmark Gillespie algorithm,but with much less running time,which seems to open new opportunities in the variational approach to efficient analysis of noisy dynamics.展开更多
Climate warming and atmospheric nitrogen(N)deposition have profound influences on the terrestrial biosphere.However,how these two global change drivers affect phytoplankton which are important primary producers in wet...Climate warming and atmospheric nitrogen(N)deposition have profound influences on the terrestrial biosphere.However,how these two global change drivers affect phytoplankton which are important primary producers in wetlands with large carbon stocks and complex hydrological fluctuations remain largely unclear.As part of a two-year field experiment in a freshwater wetland,this study was conducted to investigate the effects of nighttime warming and N addition on phytoplankton biomass in the North China Plain.The results showed that neither nighttime warming nor N addition influenced the Shannon-Wiener index of phytoplankton community.Nighttime warming did not change phytoplankton biomass,likely due to the different warming impacts on dominant phyla and in different seasons.Decreased phytoplankton biomass in spring because of the increased water pH and submerged plant coverage was compensated by the enhanced biomass in autumn due to the reduced dissolved oxygen and submerged plant coverage,leading to the neutral change of phytoplankton biomass under warming.Nitrogen addition elevated phytoplankton biomass by 11.6%,which could be attributed to the enhanced nutrient availability and reduced submerged plant coverage.Positive relationships of methane(CH4)emission rates at the water-air interface with phytoplankton biomass indicated the potentially crucial role of phytoplankton in mediating wetland CH4 cycling through photosynthesis-driven metabolisms.The findings suggested the seasonal variation of phytoplankton and their potential responses to nighttime warming and N deposition,which may provide a more accurate basis for assessing the global change-carbon feedback in wetland ecosystems.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and colla...Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.展开更多
Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of eco...Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.展开更多
The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-bas...The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.展开更多
Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph co...Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph construction,and near-complete genome assemblies now allow accurate SV detection and genotyping.These innovations overcome the limitations of single-reference genomes,enabling the discovery of complex SVs,including nested and overlapping variants,and providing access to previously inaccessible genomic regions such as centromeres and telomeres.This review highlights the current landscape of cattle SV research,with emphasis on integrating longread sequencing and pangenome frameworks to uncover breed-specific and population-level variation.While many SVs are linked to economically important traits such as feed efficiency and disease resistance,their broader regulatory impacts remain an active area of investigation.Emerging functional genomics approaches,including transcriptomics,epigenomics,and genome editing,will clarify how SVs influence gene regulation and phenotype.Looking forward,the integration of SV catalogs with multi-omics data,imputation resources,and artificial intelligence-driven models will be essential for translating discoveries into breeding and conservation applications.Integrating structural variants into breeding pipelines promises to revolutionize livestock genomics,enabling precision selection and sustainable agriculture despite challenges in cost,data sharing,and functional validation.展开更多
Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of var...Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.展开更多
文摘This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the idea Of the algorithm is applied to probability computing.
文摘The safety evaluation of engineering systems whose performance evaluation requires finite element analysis is a challenge in reliability theory.Recently,Adjusted Control Variates Technique(ACVAT)has proposed by the authors to solve this issue.ACVAT uses the results of a finite element method(FEM)model with coarse mesh density as the control variates of the model with fine mesh and efficiently solves FEM-based reliability problems.ACVAT however does not provide any results about the reliability-based mesh convergence of the problem,which is an important tool in FEM.Mesh-refinement analysis allows checking whether the numerical solution is sufficiently accurate,even though the exact solution is unknown.In this study,by introducing expanded control variates(ECV)formulation,ACVAT is improved and the capabilities of the method are also extended for efficient reliability mesh convergence analysis ofFEM-based reliability problems.In the present study,the FEM-based reliability analyses of four practical engineering problems are investigated by this method and the corresponding results are compared with accurate results obtained by analytical solutions for two problems.The results confirm that the proposed approach not only handles the mesh refinement progress with the required accuracy,but it also reduces considerably the computational cost of FEM-based reliability problems.
基金Supported by the National Natural Science Foundation of China(12226412)Natural Science Foundation of Jiangsu Province(BK20221339)。
文摘In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solution sequence with negative energy for the system via a variant Clark’s theorem.Then we get the asymptotical property of the solution sequence by L∞norm.
文摘Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.
基金supported financially by the National Natural Science Foundation of China(31811530297 and 32170217).
文摘Plastome variation,including single spontaneous nucleotide substitutions and single insertions/deletions,is the major source of leaf variegation in plants.Additionally,one recent study has showed that a simple plastome structural variation,which is induced by one pair of small inverted repeats,can also result in leaf variegation.Here we show a complex plastome structural variation caused by intermolecular and intramolecular recombination across three pairs of small inverted repeats accounts for leaf variegation in a widely cultivated shrub Heptapleurum ellipticum(Araliaceae).This plastome structural variation contains two deletions and two duplications,resulting in dramatic expansion of IRs,substantial contraction of LSC and loss of 11 genes that essential for photosynthesis.Plastome heteroplasmy was detected in both green and albino sectors of variegated leaves.Relative to green sectors,albino sectors in the variegated leaves exhibit significantly reduced expression for the 11 genes lost in the mutated plastome as well as 26 other genes,but significantly increased expression for one gene related to translation apparatus.Optical and transmission electron microscopy observations showed that mesophyll cells of albino sectors possess plastids lacking grana lamellae,which likely carry the mutated plastome and contribute to albinism.In both sectors,the first layer of spongy mesophyll cells beneath the lower epidermis contains normal chloroplasts,suggesting periclinal division of the lower epidermis during development.Our study demonstrates that multiple small repeats can collectively mediate intra-and inter-molecular recombination in plastome and offers a new mechanism accounting for leaf variegation in plants.
基金supported by the National Natural Science Founda-tion of China(Nos.42327806 and 52370108)Zhejiang Provincial Leading Project for Leading Geese Plan(No.2025C03073).
文摘Carbonyls play an important role in the atmospheric chemistry as the main precursor for reactive radicals and peroxyacetyl nitrate.However,little research has been conducted so far on the seasonal variations of carbonyls in China’s urban atmosphere.In this study,ambient carbonyls were 24-hourly observed in four seasons in Hangzhou,a mega-city in the Yangtze River Delta Region,China.The concentration of total carbonyls was the highest in summer(44.35μg/m^(3)),the second in winter(44.05μg/m^(3)),the third in spring(29.31μg/m^(3))and autumn(27.11μg/m^(3)).The most abundant species were found to be acetone in spring,summer,and winter,while formaldehyde in autumn.Rainfall can significantly reduce the concentrations of most ambient carbonyls,with the largest decrease observed in the wet precipitation events occurring in spring and summer,while acetone concentrations remained invariable due to its lower water solubility.Multiple linear regression analysis and carbonyls ratios indicated that anthropogenic emissions were the predominant sources of carbonyls,and atmospheric formaldehyde was mainly emitted from primary sources other than secondary sources.Vehicular exhaust was identified as the primary source of ambient carbonyls,particularly in winter,and its contribution reached 92.80%to formaldehyde.Additionally,photochemical reactions were closely associated with the secondary production of formaldehyde in summer.Carbonyls showed strong ozone formation potential in all four seasons.Based on the health risk assessment,the exposure to ambient carbonyls is harmful to outdoor pedestrians.The results could provide essential information and references for simulating regional air quality and analyzing ozone pollution,which is essential for improving air quality in the Yangtze River Delta region.
基金supported by the Science and Technology Major Project of Shanxi Province,China(202201140601025-2,202302140601001)the Agricultural Science Research Project of Shanxi Agricultural University,China(2023BQ108)+1 种基金the Senior Foreign Experts Introducing Project,China(G202204011L)the Science and Technology Innovation Young Talent Team of Shanxi Province,China(202204051001019)。
文摘Structural variation is an important source of genetic variation in wheat and have been important in the evolution of the wheat's genome.Few studies have examined the relationship between structural variations and agronomy and drought tolerance.The present study identified structural chromosome variations(SCVs)in a doubled haploid(DH)population and backcross introgression lines(BC5F3)derived from Jinmai 47 and Jinmai 84 using fluorescence in situ hybridization(FISH).There are one simple translocation,10 present/absent variations(PAVs),and one copy number variation(CNV)between Jinmai 47 and Jinmai 84,which distributed in 10 chromosomes.Eight SCVs were associated with 15 agronomic traits.A PAV recombination occurred on chromosome 2A,which was associated with grain number per spike(GNS).The 1BL/1RS translocation and PAV.2D were associated with significant reductions in plant height,deriving from the effects on LI2-LI4,LI2-LI4 and UI,respectively respectively.PAV.2D was also contributed to an increase of 3.13%for GNS,1BL/1RS significantly increased spikelet number,grain length(GL),and grain thickness(GT).The effect of PAV.4A.1 on GL,PAV.6A on spike length(SL)and thousand-grain weight(TGW),PAV.6B on SL,GT and TGW were identified and verified.PAVs on chromosomes 2A,6A,1D,2D,and a CNV on chromosome 4B were associated with the drought tolerance coefficients.Additive and interaction effects among SCVs were observed.Many previously cloned key genes and yield-related QTL were found in polymorphic regions of PAV.2B,PAV.2D,and CNV.4B.Altogether,this study confirmed the genetic effect of SCVs on agronomy and drought tolerance,and identification of these SCVs will facilitate genetic improvement of wheat through marker-assisted selection.
基金funded by the National Natural Science Foundation of China (52061020).
文摘Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.
基金supported by the Key Research and Development Plan Project of Heilongjiang Province (2022ZX02C13)。
文摘Xishui National Forest Park in Heilongjiang Province hosts China's most pristine temperate forests and serves as a key site for ecotourism and forest therapy.However,the emission patterns of phytoncides(key bio active compounds) remain poorly understood,limiting their therapeutic application.This study provides the first comprehensive characterization of spatiotemporal dynamics in airborne phytoncides and their synergistic interactions with environmental factors throughout the autumn-early spring seasonal transition in a temperate forest ecosystem.We analyzed the compositional dynamics of phytoncides and terpenoid content variations using thermal desorption-gas chromatography-mass spectrometry(TD-GC-MS) from September 2024 to March 2025.This period encompassed seasonal transitions from autumn to early spring,including diurnal variations in September and snowfall events in November.The method demonstrated detection limits(LODs) ranging from 1.35 to 5.33 ng m-3 and quantification limits(LOQs) from 4.09 to 16.15 ng m-3.Our results revealed pronounced seasonal fluctuations in phytoncide composition.In September,terpenoids,esters,alcohols,and alkanes displayed a diurnal "decrease-increase" trend,whereas aldehydes and ketones peaked at midday.Notably,esters and alcohols were undetectable in November and January.By January,terpenoids reached their lowest proportion(0.17±0.02%) at noon.Five terpenoids(α-pinene,myrcene,D-limonene,camphene,p-cymene) were detected in September,four(α-pinene,D-limonene,camphene,p-cymene) in November,two(D-limonene,p-cymene) in January,and only p-cymene in March.The total concentration and emission rate of the five terpenoids peaked in September afternoons at 1961.58±106.67 ng m^(-3) and653.86±35.56 ng m^(-3) h^(-1),respectively.Nocturnal emissions(32131.95±2522.21 ng m^(-3)) significantly surpassed daytime levels(14473.04±958.49 ng m^(-3)),with emission rates escalating from 1447.30±95.85 ng m^(-3) h^(-1)(day) to 5355.33±420.37 ng m^(-3) h^(-1)(night),marking a3.7-fold increase.Snowfall dramatically elevated terpenoid concentrations(pre-snowfall:158.58±14.12 ng m^(-3);post-snowfall:1080.57±57.76 ng m^(-3)) and emission rates(pre-snowfall:52.86±4.71 ng m^(-3) h^(-1);post-snowfall:360.19±19.25 ng m^(-3) h^(-1)),reflecting a 6.8-fold surge.This study underscores the profound influence of light intensity,seasonal shifts,and climatic conditions on airborne phytoncide levels,offering a scientific foundation for optimizing forest therapy and ecotourism strategies.
基金supported by the National Crop Germplasm Resources Center(NCGRC-2024-056)the National Natural Science Foundation of China(32301813).
文摘Finger millet(Eleusine coracana Gaertn.),a nutritionally rich and drought-resilient C4 cereal,possesses exceptional grain storage longevity(up to 50 years).Here,we report a high-quality genome assembly of the allotetraploid cultivar C142,revealing extensive structural rearrangements between its two subgenomes(subA and subB),which are associated with asymmetric gene expression and subgenome dominance favoring subA.SubB diverged from subA and E.indica approximately 6.8 million years ago.Subsequently,two whole-genome duplication events shaped the current genome architecture,contributing to gene redundancy and adaptive potential.Notably,expansion of stress-related gene families,such as aldo-keto reductases,suggests a role in oxidative stress response and drought adaptation.Using genome-wide association studies,we identify several candidate genes associated with key agronomic traits.Among them,EcMDHAR,encoding monodehydroascorbate reductase,plays a critical role in enhancing drought tolerance.Different EcMDHAR haplotypes exhibit distinct expression profiles,supporting their functional relevance in drought adaptation.This genomic resource not only advances our understanding of polyploid genome evolution in millets,but also provides a foundation for genome-assisted improvement of drought resistance and nutritional quality in finger millet.
文摘Cynomolgus macaques,a species of Old World primate native to southeastern and eastern Asia and the island of Mauritius,are one of the most important nonhuman primate models for infectious disease.Although the closely related rhesus macaque is classified into subspecies based on geographic origin,no such subdivision exists for cynomolgus macaques,and they continue to be used interchangeably in infectious disease research,reducing the comparability of data produced from these studies.Research into the population genetics of cynomolgus macaques has found significant differences between macaques native to different areas,including their genetic diversity,with macaques from insular populations such as Mauritius and the Philippines exhibiting highly restricted heterozygosity compared to mainland populations native to Indonesia or Cambodia.In the context of infectious disease studies,research into pathogens,including Ebola virus,Crimean-Congo hemorrhagic fever virus,and Mycobacterium tuberculosis have found differences in study outcomes,survival times,and immune cell responses between different populations of macaques.This review provides an overview of the differences between cynomolgus macaque populations in the context of genetic diversity,and in response to infection,and highlights the need for clear reporting of geographic origin of primates used in research.This will improve data comparison between studies and help to further refine this important animal model.
基金supported by the National Natural Science Foundation of China(Nos.42305147 and 42405138)the Natural Science Foundation of Jiangsu Province(No.BK20230428).
文摘Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.
基金supported by the National Natural Science Foundation of China under Grants No.12375030。
文摘Signal transduction in a cell is mostly mediated with biochemical reactions which are noisy and often modeled with chemical master equations or Langevin type of dynamics.Thus stochastic simulation constitutes a major part of computation in cell signaling.Nevertheless,the presence of a wide span of time scales or molecular numbers in various pathways may lead to trouble in computation efficiency or accuracy.To avoid this problem,the commonly employed variational method evolves the whole probability distribution and reduces the stochastic equations to deterministic ones of only a few parameters.However,the design of the left variational basis is essential for its successful application,especially to large networks.In this paper,we extend the conventional polynomial basis to the Fourier and further the Gaussian basis,much facilitating description of multi-peaked or localized non-Gaussian distributions and at the same time avoiding numerical instability and computational complexity frequently encountered with conventional basis.The extension here is demonstrated in several typical biochemical signaling networks and achieves similar accuracy as the benchmark Gillespie algorithm,but with much less running time,which seems to open new opportunities in the variational approach to efficient analysis of noisy dynamics.
基金supported by the Science and Technology Project of Hebei Education Department(No.QN2023028)the Natural Science Foundation of Hebei Province(No.C2022201042)+1 种基金the High-level Talent Research Funding Project of Hebei University(Nos.521000981405 and 521000981186)the Collaborative Innovation Center for Baiyangdian Basin Ecological Protection and Beijing-Tianjin-Hebei Sustainable Development.
文摘Climate warming and atmospheric nitrogen(N)deposition have profound influences on the terrestrial biosphere.However,how these two global change drivers affect phytoplankton which are important primary producers in wetlands with large carbon stocks and complex hydrological fluctuations remain largely unclear.As part of a two-year field experiment in a freshwater wetland,this study was conducted to investigate the effects of nighttime warming and N addition on phytoplankton biomass in the North China Plain.The results showed that neither nighttime warming nor N addition influenced the Shannon-Wiener index of phytoplankton community.Nighttime warming did not change phytoplankton biomass,likely due to the different warming impacts on dominant phyla and in different seasons.Decreased phytoplankton biomass in spring because of the increased water pH and submerged plant coverage was compensated by the enhanced biomass in autumn due to the reduced dissolved oxygen and submerged plant coverage,leading to the neutral change of phytoplankton biomass under warming.Nitrogen addition elevated phytoplankton biomass by 11.6%,which could be attributed to the enhanced nutrient availability and reduced submerged plant coverage.Positive relationships of methane(CH4)emission rates at the water-air interface with phytoplankton biomass indicated the potentially crucial role of phytoplankton in mediating wetland CH4 cycling through photosynthesis-driven metabolisms.The findings suggested the seasonal variation of phytoplankton and their potential responses to nighttime warming and N deposition,which may provide a more accurate basis for assessing the global change-carbon feedback in wetland ecosystems.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金supported by the National Natural Science Foundation of China Youth Fund(No.62101579)。
文摘Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
基金supported by the National Natural Science Foundation of China(U2344201 and 42101316)the Natural Science Foundation of Hunan Province,China(2022JJ40866)the Outstanding Youth Project of Education Bureau of Hunan Province,China(20B613)。
文摘Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.
基金supported by the CAS Pioneer Hundred Talents Program and Second Tibetan Plateau Scientific Expedition Research Program(2019QZKK0708)as well as the Basic Research Program of Qinghai Province:Lithospheric Geomagnetic Field of the Qinghai-Tibet Plateau and the Relationship with Strong Earthquakes(2021-ZJ-969Q).
文摘The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.
基金supported in part by AFRI grant numbers 2019-7015-29321 and 2021-67015-33409 from the USDA National Institute of Food and Agriculture(NIFA)the SCINet project of the USDA ARS project number 0500-00093-001-00-D。
文摘Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph construction,and near-complete genome assemblies now allow accurate SV detection and genotyping.These innovations overcome the limitations of single-reference genomes,enabling the discovery of complex SVs,including nested and overlapping variants,and providing access to previously inaccessible genomic regions such as centromeres and telomeres.This review highlights the current landscape of cattle SV research,with emphasis on integrating longread sequencing and pangenome frameworks to uncover breed-specific and population-level variation.While many SVs are linked to economically important traits such as feed efficiency and disease resistance,their broader regulatory impacts remain an active area of investigation.Emerging functional genomics approaches,including transcriptomics,epigenomics,and genome editing,will clarify how SVs influence gene regulation and phenotype.Looking forward,the integration of SV catalogs with multi-omics data,imputation resources,and artificial intelligence-driven models will be essential for translating discoveries into breeding and conservation applications.Integrating structural variants into breeding pipelines promises to revolutionize livestock genomics,enabling precision selection and sustainable agriculture despite challenges in cost,data sharing,and functional validation.
基金Project supported in part by Beijing Natural Science Foundation(Grant No.1232025)Peng Huanwu Visiting Pro-fessor Program,and Academy for Multidisciplinary Studies,Capital Normal University.
文摘Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.
