Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrat...Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.展开更多
Objective:Gut microbiota(GM)and blood metabolites are associated with the development of urticaria,yet their specific causal relationships in East Asian populations remain unclear.This study aims to elucidate the caus...Objective:Gut microbiota(GM)and blood metabolites are associated with the development of urticaria,yet their specific causal relationships in East Asian populations remain unclear.This study aims to elucidate the causal and mediating relationships among GM,blood metabolites,and urticaria in East Asians using Mendelian randomization(MR)analysis.Methods:Summary-level statistics for 500 GM taxa,112 blood metabolites,and urticaria were obtained from publicly available Genome-Wide Association Studies(GWAS)datasets.Bidirectional MR analyses were performed to examine causal associations among the GM,blood metabolites,and urticaria.The inverse variance weighted(IVW)method served as the primary analytical approach,supplemented by MR-Egger,weighted median,simple mode,and weighted mode methods.Sensitivity analyses included heterogeneity tests,horizontal pleiotropy assessments,and leave-one-out analyses.Mediation analysis was conducted to evaluate the potential mediating effects of blood metabolites on the causal pathways between GM and urticaria.Results:MR analyses identified 12 GM taxa exhibiting significant causal effects on urticaria susceptibility.Nine taxa,such as MF0017_galactose_degradation(OR=1.461,95%CI 1.098 to 1.944,P=0.009),were associated with increased urticaria risk.Three taxa,such as MF0001_arabinoxylan_degradation(OR=0.846,95%CI 0.737 to 0.973,P=0.019),showed protective effects with increased abundance.Additionally,6 blood metabolites demonstrated causal associations with urticaria.Notably,the risk of developing urticaria increases with rising fasting plasma glucose(FPG)levels(OR=1.971,95%CI 1.089 to 3.567,P=0.025).Mediation analysis further demonstrated that FPG partially mediated the protective effect of MF0001_arabinoxylan_degradation on urticaria,accounting for 11.30%of the total effect.Conclusion:This study has delineated specific GM taxa and blood metabolites that hold causal relevance to urticaria in East Asian populations.Notably,arabinogalactan degradation potentially mitigates urticaria risk via reducing FPG concentrations,offering genetic evidence to support therapeutic strategies targeting GM modulation and glucose regulation.展开更多
Objective:The causal relationship between eczema and autoimmune diseases has not been previously reported.This study aims to evaluate the causal relationship between eczema and autoimmune diseases.Methods:The two‐sam...Objective:The causal relationship between eczema and autoimmune diseases has not been previously reported.This study aims to evaluate the causal relationship between eczema and autoimmune diseases.Methods:The two‐sample Mendelian randomization(MR)method was used to assess the causal effect of eczema on autoimmune diseases.Summary data from the Genome-Wide Association Study Catalog(GWAS)were obtained from the Integrative Epidemiology Unit(IEU)database.For eczema and autoimmune diseases,genetic instrument variants(GIVs)were identified according to the significant difference(P<5×10−8).Causal effect estimates were generated using the inverse‐variance weighted(IVW)method.MR Egger,maximum likelihood,MR-PRESSO,and MR-RAPS methods were used for alternative analyses.Sensitivity tests,including heterogeneity,horizontal pleiotropy,and leave-one-out analyses,were performed.Finally,reverse causality was assessed.Results:Genetic susceptibility to eczema was associated with an increased risk of Crohn’s disease(OR=1.444,95%CI 1.199 to 1.738,P<0.001)and ulcerative colitis(OR=1.002,95%CI 1.001 to 1.003,P=0.002).However,no causal relationship was found for the other 6 autoimmune diseases,including systemic lupus erythematosus(SLE)(OR=0.932,P=0.401),bullous pemphigoid(BP)(OR=1.191,P=0.642),vitiligo(OR=1.000,P=0.327),multiple sclerosis(MS)(OR=1.000,P=0.965),ankylosing spondylitis(AS)(OR=1.001,P=0.121),rheumatoid arthritis(RA)(OR=1.000,P=0.460).Additionally,no reverse causal relationship was found between autoimmune diseases and eczema.Conclusion:Eczema is associated with an increased risk of Crohn’s disease and ulcerative colitis.No causal relationship is found between eczema and SLE,MS,AS,RA,BP,or vitiligo.展开更多
Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violat...Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violations of these MR assumptions through sensitivity analysis.Sensitivity analyses have been conducted for simple MR-based causal average effect analyses,but they are not available for MR-based mediation analysis studies,and we aim to fill this gap in this paper.We propose to use two sensitivity parameters to quantify the effect due to the deviation of the IV assumptions.With these two sensitivity parameters,we derive consistent indirect causal effect estimators and establish their asymptotic propersties.Our theoretical results can be used in MR-based mediation analysis to study the impact of violations of MR as-sumptions.The finite sample performance of the proposed method is illustrated through simulation studies,sensitivity ana-lysis,and application to a real genome-wide association study.展开更多
We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the ...We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.展开更多
Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To sp...Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.展开更多
A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in peri...A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.展开更多
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for mari...Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.展开更多
Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnormalities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)hav...Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnormalities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms underlying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Utilizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expression QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4110,NControls=13569),we implemented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Association Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD pathogenesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate<0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were associated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were associated with CJD at the transcriptional level.展开更多
Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrh...Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrhagic stroke remains unknown.This study aimed to investigate the potential causal relationship between antibody-mediated immune responses to infectious agents and hemorrhagic stroke using the two-sample Mendelian randomization(MR)method.Comprehensive analyses were conducted using publicly available data from genome-wide association study(GWAS),which involved the whole genomes of 9724 European participants and 46 antibody measurement phenotypes,and summary statistics from the FinnGen dataset R12(including intracerebral hemorrhage and subarachnoid hemorrhage)were used.The causal relationship between the aforementioned immune responses and hemorrhagic stroke was analyzed using inverse-variance weighting,MR-Egger regression,weighted median,weighted mode,simple mode,and MR-pleiotropy residual sum and outlier(MR-PRESSO),while various sensitivity analyses were performed to assess heterogeneity and pleiotropy in the study findings.Results showed that human herpes virus 7(HHV-7)U14 antibody levels(OR:0.877,95%CI:0.797-0.964,P=0.007)exerted a protective effect against hemorrhagic stroke,and Chlamydia trachomatis(CT)tarp-D F2 antibody levels(OR:0.937,95%CI:0.885-0.992,P=0.025)had a potential protective effect;additionally,Epstein-Barr virus(EBV)ZEBRA antibody levels(OR:1.062,95%CI:1.012-1.114,P=0.014),human herpesvirus 6(HHV-6)p101k antibody levels(OR:1.054,95%CI:1.002-1.108,P=0.042),and cytomegalovirus(CMV)pp150 antibody levels(OR:1.086,95%CI:1.002-1.176,P=0.045)were potential risk factors for the disease.No significant pleiotropy or heterogeneity was observed in any of the MR analyses.Collectively,these findings confirmed a significant causal relationship between antibody-mediated immune responses and hemorrhagic stroke,and this study contributed to a deeper understanding of the potential mechanisms underlying hemorrhagic stroke onset.展开更多
OBJECTIVE To investigate the intervention effects of tissue-bone homeostasis manipulation(TBHM)on peripatellar biomechanical parameters and knee joint function in knee osteoarthritis(KOA)patients.METHODS Sixty patient...OBJECTIVE To investigate the intervention effects of tissue-bone homeostasis manipulation(TBHM)on peripatellar biomechanical parameters and knee joint function in knee osteoarthritis(KOA)patients.METHODS Sixty patients with KOA(Kellgren-Lawrence gradeⅡ-Ⅲ)were recruited from the Acupuncture-Moxibustion Rehabilitation Department,Anhui University of Chinese Medicine between October 2024 and May 2025.Participants were randomized into a TBHM group(n=30)or a transcutaneous electrical neuromuscular stimulation(TENS)group(n=30).Using two-way repeated measures ANOVA,biomechanical indicators,including rectus femoris tension,vastus medialis tension,vastus lateralis tension,patellar ligament tension,lateral patellar displacement(LPD),medial patellar displacement(MPD),normalized patellar mobility(LPD/patellar width[PW],MPD/PW),knee flexion range of motion,and functional indicators,including KOOS subscales,time up and go test(TUGT),were compared between groups at baseline and after 6 weeks of intervention.RESULTS After intervention,all biomechanical and knee joint function indicators in the TBHM group were significantly improved(P<0.05,P<0.01),while only the vastus medialis tension,TUGT and KOOS Pain,ADL and QoL scores in the control group were significantly improved(P<0.01).The improvement amplitudes of biomechanical indicators in the TBHM group,including rectus femoris tension,vastus lateralis tension,patellar ligament tension,MPD/PW,LPD/PW and knee flexion range of motion were better than those in the control group(P<0.05,P<0.01).In the functional evaluation,the interaction effects of the TBHM group in all dimensions of the KOOS score and TUGT were statistically significant(P<0.05,P<0.01).Post-hoc simple effect analysis confirmed that there were significant differences in the above indicators between the two groups after intervention(P<0.05),and all indicators showed a significant main effect of time(P<0.01),suggesting that the intervention measures had continuous and cumulative curative effects.CONCLUSION TBHM effectively improves joint function and quality of life in KOA patients by restoring dynamic equilibrium in soft tissue tension and patellar mobility,ultimately achieving the therapeutic goal of concurrent tissue-bone management.展开更多
A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
In this paper,large deviations principle(LDP)and moderate deviations principle(MDP)of record numbers in random walks are studied under certain conditions.The results show that the rate functions of LDP and MDP are dif...In this paper,large deviations principle(LDP)and moderate deviations principle(MDP)of record numbers in random walks are studied under certain conditions.The results show that the rate functions of LDP and MDP are different from those of weak record numbers,which are interesting complements of the conclusions by Li and Yao[1].展开更多
Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneit...Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.展开更多
The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-...Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-altitude environmental observation and target detection and tracking.Existing studies primarily focus on specific airspace regions,leaving critical gaps in understanding the effects of long dispersion times,wide altitude ranges,and variable atmospheric conditions on missile contrail clouds.To address these gaps,this article develops a numerical method based on the Lagrangian random walk model,which incorporates various velocity variation terms,including particle velocity caused by the difference of wind field,by the thermal motion of local gas molecules and by random collisions between contrail cloud particles to capture the influence of environmental wind fields,atmospheric conditions,and particle concentrations on the motion of contrail cloud particles.A general coordinate system aligned with the missile's flight trajectory is employed to represent particle distribution characteristics.The proposed method is in good agreement with the conducted experiments as well as with the available numerical simulations.The results demonstrate that the proposed model effectively simulates the dispersion state of contrail clouds,accurately reflecting the impact of large-scale wind field variations and altitude changes with high computational efficiency.Additionally,simulation results indicate that the increased distance between gas molecules in rarefied environments facilitates enhanced particle dispersion,while larger particles exhibit a faster dispersion rate due to their greater mass.展开更多
This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled a...This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.展开更多
基金supported by the Natural Science Foundation of Hunan Province (2022JJ30987)the Key Research and Development Project of Hunan Province (2024JK2107),China。
文摘Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.
基金supported by the Natural Science Foundation of Hunan Province,China(2024JJ7627).
文摘Objective:Gut microbiota(GM)and blood metabolites are associated with the development of urticaria,yet their specific causal relationships in East Asian populations remain unclear.This study aims to elucidate the causal and mediating relationships among GM,blood metabolites,and urticaria in East Asians using Mendelian randomization(MR)analysis.Methods:Summary-level statistics for 500 GM taxa,112 blood metabolites,and urticaria were obtained from publicly available Genome-Wide Association Studies(GWAS)datasets.Bidirectional MR analyses were performed to examine causal associations among the GM,blood metabolites,and urticaria.The inverse variance weighted(IVW)method served as the primary analytical approach,supplemented by MR-Egger,weighted median,simple mode,and weighted mode methods.Sensitivity analyses included heterogeneity tests,horizontal pleiotropy assessments,and leave-one-out analyses.Mediation analysis was conducted to evaluate the potential mediating effects of blood metabolites on the causal pathways between GM and urticaria.Results:MR analyses identified 12 GM taxa exhibiting significant causal effects on urticaria susceptibility.Nine taxa,such as MF0017_galactose_degradation(OR=1.461,95%CI 1.098 to 1.944,P=0.009),were associated with increased urticaria risk.Three taxa,such as MF0001_arabinoxylan_degradation(OR=0.846,95%CI 0.737 to 0.973,P=0.019),showed protective effects with increased abundance.Additionally,6 blood metabolites demonstrated causal associations with urticaria.Notably,the risk of developing urticaria increases with rising fasting plasma glucose(FPG)levels(OR=1.971,95%CI 1.089 to 3.567,P=0.025).Mediation analysis further demonstrated that FPG partially mediated the protective effect of MF0001_arabinoxylan_degradation on urticaria,accounting for 11.30%of the total effect.Conclusion:This study has delineated specific GM taxa and blood metabolites that hold causal relevance to urticaria in East Asian populations.Notably,arabinogalactan degradation potentially mitigates urticaria risk via reducing FPG concentrations,offering genetic evidence to support therapeutic strategies targeting GM modulation and glucose regulation.
基金This work was supported by the National Natural Science Foundation (82273506,82273508)the Hunan Provincial Health Commission Scientific Research Plan Project (D202304128334),China。
文摘Objective:The causal relationship between eczema and autoimmune diseases has not been previously reported.This study aims to evaluate the causal relationship between eczema and autoimmune diseases.Methods:The two‐sample Mendelian randomization(MR)method was used to assess the causal effect of eczema on autoimmune diseases.Summary data from the Genome-Wide Association Study Catalog(GWAS)were obtained from the Integrative Epidemiology Unit(IEU)database.For eczema and autoimmune diseases,genetic instrument variants(GIVs)were identified according to the significant difference(P<5×10−8).Causal effect estimates were generated using the inverse‐variance weighted(IVW)method.MR Egger,maximum likelihood,MR-PRESSO,and MR-RAPS methods were used for alternative analyses.Sensitivity tests,including heterogeneity,horizontal pleiotropy,and leave-one-out analyses,were performed.Finally,reverse causality was assessed.Results:Genetic susceptibility to eczema was associated with an increased risk of Crohn’s disease(OR=1.444,95%CI 1.199 to 1.738,P<0.001)and ulcerative colitis(OR=1.002,95%CI 1.001 to 1.003,P=0.002).However,no causal relationship was found for the other 6 autoimmune diseases,including systemic lupus erythematosus(SLE)(OR=0.932,P=0.401),bullous pemphigoid(BP)(OR=1.191,P=0.642),vitiligo(OR=1.000,P=0.327),multiple sclerosis(MS)(OR=1.000,P=0.965),ankylosing spondylitis(AS)(OR=1.001,P=0.121),rheumatoid arthritis(RA)(OR=1.000,P=0.460).Additionally,no reverse causal relationship was found between autoimmune diseases and eczema.Conclusion:Eczema is associated with an increased risk of Crohn’s disease and ulcerative colitis.No causal relationship is found between eczema and SLE,MS,AS,RA,BP,or vitiligo.
基金This work was supported by the National Natural Science Foundation of China(12171451,72091212).
文摘Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violations of these MR assumptions through sensitivity analysis.Sensitivity analyses have been conducted for simple MR-based causal average effect analyses,but they are not available for MR-based mediation analysis studies,and we aim to fill this gap in this paper.We propose to use two sensitivity parameters to quantify the effect due to the deviation of the IV assumptions.With these two sensitivity parameters,we derive consistent indirect causal effect estimators and establish their asymptotic propersties.Our theoretical results can be used in MR-based mediation analysis to study the impact of violations of MR as-sumptions.The finite sample performance of the proposed method is illustrated through simulation studies,sensitivity ana-lysis,and application to a real genome-wide association study.
基金supported by Xinjiang Normal University Outstanding Young Teacher Research Launch Fund Project(Grant No.XJNU202116)。
文摘We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.
文摘Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.
基金Supported by the National Natural Science Foundation of China(32072352)the National Key Research and Development Program Project of China(2022YFD1600500)。
文摘A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
文摘Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.
文摘Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnormalities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms underlying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Utilizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expression QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4110,NControls=13569),we implemented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Association Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD pathogenesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate<0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were associated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were associated with CJD at the transcriptional level.
基金Supported by the National Natural Science Foundations of China(82271340,82071368)。
文摘Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrhagic stroke remains unknown.This study aimed to investigate the potential causal relationship between antibody-mediated immune responses to infectious agents and hemorrhagic stroke using the two-sample Mendelian randomization(MR)method.Comprehensive analyses were conducted using publicly available data from genome-wide association study(GWAS),which involved the whole genomes of 9724 European participants and 46 antibody measurement phenotypes,and summary statistics from the FinnGen dataset R12(including intracerebral hemorrhage and subarachnoid hemorrhage)were used.The causal relationship between the aforementioned immune responses and hemorrhagic stroke was analyzed using inverse-variance weighting,MR-Egger regression,weighted median,weighted mode,simple mode,and MR-pleiotropy residual sum and outlier(MR-PRESSO),while various sensitivity analyses were performed to assess heterogeneity and pleiotropy in the study findings.Results showed that human herpes virus 7(HHV-7)U14 antibody levels(OR:0.877,95%CI:0.797-0.964,P=0.007)exerted a protective effect against hemorrhagic stroke,and Chlamydia trachomatis(CT)tarp-D F2 antibody levels(OR:0.937,95%CI:0.885-0.992,P=0.025)had a potential protective effect;additionally,Epstein-Barr virus(EBV)ZEBRA antibody levels(OR:1.062,95%CI:1.012-1.114,P=0.014),human herpesvirus 6(HHV-6)p101k antibody levels(OR:1.054,95%CI:1.002-1.108,P=0.042),and cytomegalovirus(CMV)pp150 antibody levels(OR:1.086,95%CI:1.002-1.176,P=0.045)were potential risk factors for the disease.No significant pleiotropy or heterogeneity was observed in any of the MR analyses.Collectively,these findings confirmed a significant causal relationship between antibody-mediated immune responses and hemorrhagic stroke,and this study contributed to a deeper understanding of the potential mechanisms underlying hemorrhagic stroke onset.
文摘OBJECTIVE To investigate the intervention effects of tissue-bone homeostasis manipulation(TBHM)on peripatellar biomechanical parameters and knee joint function in knee osteoarthritis(KOA)patients.METHODS Sixty patients with KOA(Kellgren-Lawrence gradeⅡ-Ⅲ)were recruited from the Acupuncture-Moxibustion Rehabilitation Department,Anhui University of Chinese Medicine between October 2024 and May 2025.Participants were randomized into a TBHM group(n=30)or a transcutaneous electrical neuromuscular stimulation(TENS)group(n=30).Using two-way repeated measures ANOVA,biomechanical indicators,including rectus femoris tension,vastus medialis tension,vastus lateralis tension,patellar ligament tension,lateral patellar displacement(LPD),medial patellar displacement(MPD),normalized patellar mobility(LPD/patellar width[PW],MPD/PW),knee flexion range of motion,and functional indicators,including KOOS subscales,time up and go test(TUGT),were compared between groups at baseline and after 6 weeks of intervention.RESULTS After intervention,all biomechanical and knee joint function indicators in the TBHM group were significantly improved(P<0.05,P<0.01),while only the vastus medialis tension,TUGT and KOOS Pain,ADL and QoL scores in the control group were significantly improved(P<0.01).The improvement amplitudes of biomechanical indicators in the TBHM group,including rectus femoris tension,vastus lateralis tension,patellar ligament tension,MPD/PW,LPD/PW and knee flexion range of motion were better than those in the control group(P<0.05,P<0.01).In the functional evaluation,the interaction effects of the TBHM group in all dimensions of the KOOS score and TUGT were statistically significant(P<0.05,P<0.01).Post-hoc simple effect analysis confirmed that there were significant differences in the above indicators between the two groups after intervention(P<0.05),and all indicators showed a significant main effect of time(P<0.01),suggesting that the intervention measures had continuous and cumulative curative effects.CONCLUSION TBHM effectively improves joint function and quality of life in KOA patients by restoring dynamic equilibrium in soft tissue tension and patellar mobility,ultimately achieving the therapeutic goal of concurrent tissue-bone management.
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金supported by the National Natural Science Foundation of China(Grant No.11671145)the Science and Technology Commission of Shanghai Municipality(Grant No.18dz2271000).
文摘In this paper,large deviations principle(LDP)and moderate deviations principle(MDP)of record numbers in random walks are studied under certain conditions.The results show that the rate functions of LDP and MDP are different from those of weak record numbers,which are interesting complements of the conclusions by Li and Yao[1].
基金Supported by the National Natural Science Foundation of China(42401488,42071351)the National Key Research and Development Program of China(2020YFA0608501,2017YFB0504204)+4 种基金the Liaoning Revitalization Talents Program(XLYC1802027)the Talent Recruited Program of the Chinese Academy of Science(Y938091)the Project Supported Discipline Innovation Team of the Liaoning Technical University(LNTU20TD-23)the Liaoning Province Doctoral Research Initiation Fund Program(2023-BS-202)the Basic Research Projects of Liaoning Department of Education(JYTQN2023202)。
文摘Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
文摘Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-altitude environmental observation and target detection and tracking.Existing studies primarily focus on specific airspace regions,leaving critical gaps in understanding the effects of long dispersion times,wide altitude ranges,and variable atmospheric conditions on missile contrail clouds.To address these gaps,this article develops a numerical method based on the Lagrangian random walk model,which incorporates various velocity variation terms,including particle velocity caused by the difference of wind field,by the thermal motion of local gas molecules and by random collisions between contrail cloud particles to capture the influence of environmental wind fields,atmospheric conditions,and particle concentrations on the motion of contrail cloud particles.A general coordinate system aligned with the missile's flight trajectory is employed to represent particle distribution characteristics.The proposed method is in good agreement with the conducted experiments as well as with the available numerical simulations.The results demonstrate that the proposed model effectively simulates the dispersion state of contrail clouds,accurately reflecting the impact of large-scale wind field variations and altitude changes with high computational efficiency.Additionally,simulation results indicate that the increased distance between gas molecules in rarefied environments facilitates enhanced particle dispersion,while larger particles exhibit a faster dispersion rate due to their greater mass.
基金Project(52178402) supported by the National Natural Science Foundation of China。
文摘This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.
