In this paper, the determinacy of regular lexical selectional restrictions is examined from both the internal structures of the single selectional restrictions (i.e.semantic selectional restrictions) and relationship ...In this paper, the determinacy of regular lexical selectional restrictions is examined from both the internal structures of the single selectional restrictions (i.e.semantic selectional restrictions) and relationship between the structures of several selectional restrictions. Hence, our analysis and description are shifted from casual and indeterminate strings and markers on case basis to determined rules and circumspect dynamic systems, from lexical precepts to knowledge precepts, from the state of memory to rational deduction and rhetorical devices. Further studies indicate that selectional restrictions are intertwined structures, a feature that makes it possible to be one of the bases for lexical selectional restrictions to come into existence. Its related theories are the grarantee for scientific observations of selectional restriction structures.展开更多
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
Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applicati...Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.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
Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The a...Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The applicability and antimicrobial efficacy of these nanomaterials were systematically investigated for metal ion sensing.Experimental evidence demonstrated that the Ag‑CDs exhibited a pronounced fluorescence quenching response toward ferric ions(Fe^(3+)),enabling their quantitative determination via a linear concentration‑dependent relationship.These Ag‑CDs exhibited significant inhibitory effects on biofilm growth and disruption for both Escherichia coli and Staphylococcus aureus.Mechanism investigations indicate that Ag‑CDs induced the death of Escherichia coli and Pseudomonas aeruginosa by disrupting their bacterial morphology and structure,triggering the generation of intracellular reactive oxygen species(ROS),and impairing their antioxidant defense system.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
By means the in situ halogenation of the vinyl C-H bond in o-hydroxyphenyl enaminones,the step efficient synthesis of 3-diphenylphosphinyl chromones has been realized through the challenging construction of C-P(Ⅲ) bo...By means the in situ halogenation of the vinyl C-H bond in o-hydroxyphenyl enaminones,the step efficient synthesis of 3-diphenylphosphinyl chromones has been realized through the challenging construction of C-P(Ⅲ) bond by using diphenyl phosphine as reaction partner.In addition,the tunable synthesis of 2-phosphoryl chromanones has been achieved via hydrophosphorylation by simply modifying reaction conditions without using metal reagent.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
Nanoporous polymers are extensively coated on various substrates to deliver optical,permselective,or other functions.However,it remains desired to fast produce uniform nanoporous polymer coatings on substrates with co...Nanoporous polymers are extensively coated on various substrates to deliver optical,permselective,or other functions.However,it remains desired to fast produce uniform nanoporous polymer coatings on substrates with complex surfaces.Herein,by manipulating the interactions between block copolymers and selective solvents,we prepare repairable nanoporous polymers on arbitrary substrates.This is realized by an extremely simple sequential coating process:sequential coating of block copolymers and their swelling agents on substrate surfaces.The swelling agents are comprised of two solvents that swell the constituent blocks of the copolymers to different degrees,rapidly producing polymer coatings with uniform,interconnected,sub-50 nm pores.This sequential coating process is able to conformally build nanoporous polymers on nonplanar substrates with large lateral sizes and complex surface features,and also to in situ repair defects arising during usages.We further demonstrate that the nanoporous coatings show excellent antireflective and membrane separation performances.This sequential coating process is dictated by polymer–solvent interactions,and is expected to find applications in diverse fields for its simplicity,adaptability,and the capability to produce well-defined nanoporosities.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
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
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.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
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
GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a ...GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.展开更多
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in...Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.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
Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(...Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.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
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
文摘In this paper, the determinacy of regular lexical selectional restrictions is examined from both the internal structures of the single selectional restrictions (i.e.semantic selectional restrictions) and relationship between the structures of several selectional restrictions. Hence, our analysis and description are shifted from casual and indeterminate strings and markers on case basis to determined rules and circumspect dynamic systems, from lexical precepts to knowledge precepts, from the state of memory to rational deduction and rhetorical devices. Further studies indicate that selectional restrictions are intertwined structures, a feature that makes it possible to be one of the bases for lexical selectional restrictions to come into existence. Its related theories are the grarantee for scientific observations of selectional restriction structures.
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(52171191,52371198)Project of Constructing National Independent Innovation Demonstration Zones(XM2024XTGXQ05)。
文摘Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.
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.
文摘Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The applicability and antimicrobial efficacy of these nanomaterials were systematically investigated for metal ion sensing.Experimental evidence demonstrated that the Ag‑CDs exhibited a pronounced fluorescence quenching response toward ferric ions(Fe^(3+)),enabling their quantitative determination via a linear concentration‑dependent relationship.These Ag‑CDs exhibited significant inhibitory effects on biofilm growth and disruption for both Escherichia coli and Staphylococcus aureus.Mechanism investigations indicate that Ag‑CDs induced the death of Escherichia coli and Pseudomonas aeruginosa by disrupting their bacterial morphology and structure,triggering the generation of intracellular reactive oxygen species(ROS),and impairing their antioxidant defense system.
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.
基金financially supported by the National Natural Science Foundation of China (No.22261026)。
文摘By means the in situ halogenation of the vinyl C-H bond in o-hydroxyphenyl enaminones,the step efficient synthesis of 3-diphenylphosphinyl chromones has been realized through the challenging construction of C-P(Ⅲ) bond by using diphenyl phosphine as reaction partner.In addition,the tunable synthesis of 2-phosphoryl chromanones has been achieved via hydrophosphorylation by simply modifying reaction conditions without using metal reagent.
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 National Science Foundation of China(22438005)the Natural Science Foundation of Jiangsu Province(BE2022056-3)is gratefully acknowledged.
文摘Nanoporous polymers are extensively coated on various substrates to deliver optical,permselective,or other functions.However,it remains desired to fast produce uniform nanoporous polymer coatings on substrates with complex surfaces.Herein,by manipulating the interactions between block copolymers and selective solvents,we prepare repairable nanoporous polymers on arbitrary substrates.This is realized by an extremely simple sequential coating process:sequential coating of block copolymers and their swelling agents on substrate surfaces.The swelling agents are comprised of two solvents that swell the constituent blocks of the copolymers to different degrees,rapidly producing polymer coatings with uniform,interconnected,sub-50 nm pores.This sequential coating process is able to conformally build nanoporous polymers on nonplanar substrates with large lateral sizes and complex surface features,and also to in situ repair defects arising during usages.We further demonstrate that the nanoporous coatings show excellent antireflective and membrane separation performances.This sequential coating process is dictated by polymer–solvent interactions,and is expected to find applications in diverse fields for its simplicity,adaptability,and the capability to produce well-defined nanoporosities.
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 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 Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
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 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partially supported by grants PID2020-115096RB-I00 and PID2023-148273NB-I00 from Ministerio de Ciencia y Universidad (MICIU/AEI)(to EMS)。
文摘GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,grant number RG-2-611-42(A.O.A.).
文摘Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.
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financially supported by National Natural Science Foundation of China(22466010)Guizhou Provincial Basic Research Program(Natural Science)ZK[2023]47 and key program ZD[2025]075+6 种基金Innovation and Entrepreneurship Project for overseas Talents in Guizhou Province[2022]02Specific Natural Science Foundation of Guizhou University(X202207)the national undergraduate innovation and entrepreneurship training program(gzugc2023006gzusc2024012)SRT project of Guizhou university(2023SRT0292023SRT024)supported by Shanghai Technical Service Center of Science and Engineering Computing,Shanghai University。
文摘Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.
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supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
