Objective:Based on multistage metabolomic profiling and Mendelian randomization analyses,the current study identified plasma metabolites that predicted the risk of developing gastric cancer(GC)and determined whether k...Objective:Based on multistage metabolomic profiling and Mendelian randomization analyses,the current study identified plasma metabolites that predicted the risk of developing gastric cancer(GC)and determined whether key metabolite levels modified the GC primary prevention effects.Methods:Plasma metabolites associated with GC risk were identified through a case-control study.Bi-directional two-sample Mendelian randomization analyses were performed to determine potential causal relationships utilizing the Shandong Intervention Trial(SIT),a nested case-control study of the Mass Intervention Trial in Linqu,Shandong province(MITS),China,the UK Biobank,and the Finn Gen project.Results:A higher genetic risk score for plasma L-aspartic acid was significantly associated with an increased GC risk in the northern Chinese population(SIT:HR=1.26 per 1 SD change,95%CI:1.07±1.49;MITS:HR=1.07,95%CI:1.00±1.14)and an increased gastric adenocarcinoma risk in Finn Gen(OR=1.68,95%CI:1.16±2.45).Genetically predicted plasma L-aspartic acid levels also modified the GC primary prevention effects with the beneficial effect of Helicobacter pylori eradication notably observed among individuals within the top quartile of L-aspartic acid level(P-interaction=0.098)and the beneficial effect of garlic supplementation only for those within the lowest quartile of L-aspartic acid level(P-interaction=0.02).Conclusions:Elevated plasma L-aspartic acid levels significantly increased the risk of developing GC and modified the effects of GC primary prevention.Further studies from other populations are warranted to validate the modification effect of plasma L-aspartic acid levels on GC prevention and to elucidate the underlying mechanisms.展开更多
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section,...To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.展开更多
In this paper, we consider the existence of pullback random exponential attractor for non-autonomous random reaction-diffusion equation driven by nonlinear colored noise defined onR^(N) . The key steps of the proof ar...In this paper, we consider the existence of pullback random exponential attractor for non-autonomous random reaction-diffusion equation driven by nonlinear colored noise defined onR^(N) . The key steps of the proof are the tails estimate and to demonstrate the Lipschitz continuity and random squeezing property of the solution for the equation defined on R^(N) .展开更多
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw...To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.展开更多
Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfiel...Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.展开更多
Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to in...Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.展开更多
Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection sei...Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.展开更多
In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random vari...In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.展开更多
Objective:To evaluate the impact of subcutaneous tunneling on peripherally inserted central catheters(PICCs)dislodgement and malposition.Dislodged or malpositioned PICCs can lead to improper treatment.The subcutaneous...Objective:To evaluate the impact of subcutaneous tunneling on peripherally inserted central catheters(PICCs)dislodgement and malposition.Dislodged or malpositioned PICCs can lead to improper treatment.The subcutaneous tunneling strategy may be effective,but there is insufficient evidence,and proximal movement has not been explored.Methods:We randomized 630 patients who needed PICCs placement to either the tunneled PICCs(experimental group)or the non-tunneled PICCs(control group).Dislodgement and malposition of the catheter were the primary outcomes,and catheter-related infection(CRI)and catheter-related thrombosis(CRT)were the secondary outcomes.Results:Subcutaneous tunneling does not significantly reduce distal catheter movement,but it significantly reduces proximal catheter movement(4.3%vs.9.9%,P=0.007),which may explain the lower incidence of CRI(2.0%vs.5.3%,P=0.030)and CRT(3.6%vs.12.5%,P<0.001).Conclusions:Although subcutaneous tunneling does not significantly improve catheter prolapse,it should still be used clinically because proximal catheter movement can be a more serious problem associated with CRI and CRT.展开更多
OBJECTIVE:To assess the effect of Traditional Chinese Medicine(TCM)treatment with syndrome differentiation in patients with severe community-acquired pneumonia(CAP).METHODS:A multicenter,randomized,placebocontrolled t...OBJECTIVE:To assess the effect of Traditional Chinese Medicine(TCM)treatment with syndrome differentiation in patients with severe community-acquired pneumonia(CAP).METHODS:A multicenter,randomized,placebocontrolled trial was conducted.Adult patients with severe CAP were randomly allocated to receive conventional medicine treatment(conventional group)or conventional medicine combine with TCM treatment with syndrome differentiation(combination group)underwent a 28-d treatment time.The primary endpoint was treatment failure.Secondary outcomes included time to clinical stability,28-d mortality,90-d mortality,length of hospital stay.RESULTS:A total of 183 patients were included in the intention-to-treat(ITT)population,with 91 allocated to the combination group and 92 to the conventional group.Patients with treatment failure in the combination group(18/91,19.8%)were lower compared with the conventional group(34/92,37.0%).Time to clinical stability was shorter in the combination group(11.8 d)than in the conventional group[17.8 d;HR=2.08,95%CI(1.38,3.14);P=0.001].The 28-d mortality was lower in the combination group(17.6%)than in the conventional group(31.5%).There was no difference in the length of hospital stay between the two groups(P=0.901).Adverse events were evenly distributed between the combination and conventional groups(P=0.837).CONCLUSION:Among patients with severe CAP,TCM treatment with syndrome differentiation reduced treatment failure,time to clinical stability,and the 28-d mortality and the 90-d mortality among patients admitted to the ward,without an increase in complications.展开更多
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
To synergistically recover alumina and alkali from red mud(RM),the structural stability and conversion mechanism of hydroandradite(HA)from hydrogarnet(HG)were investigated via the First-principles,XRF,XRD,PSD and SEM ...To synergistically recover alumina and alkali from red mud(RM),the structural stability and conversion mechanism of hydroandradite(HA)from hydrogarnet(HG)were investigated via the First-principles,XRF,XRD,PSD and SEM methods,and a novel hydrothermal process based on the conversion principle was finally proposed.The crystal structure simulation shows that the HA with varied silicon saturation coefficients is more stable than HG,and the HA with a high iron substitution coefficient is more difficult to be converted from HG.The(110)plane of Fe_(2)O_(3) is easier to combine with HG to form HA,and the binding energy is 81.93 kJ/mol.The effects of raw material ratio,solution concentration and hydrothermal parameters on the conversion from HG to HA were revealed,and the optimal conditions for the alumina recovery were obtained.The recovery efficiencies of alumina and Na_(2)O from the RM are 63.06%and 97.34%,respectively,and the Na_(2)O content in the treated RM is only 0.13%.展开更多
In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and m...In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and multiple user detection techniques,our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings,where the upper bound on performance is 1.Moreover,a lower bound on throughput performance is derived,which is tight in some parameter settings and can be used to approximate theoretical performance.Simulation results validate our analysis and confirm the advantages of our proposed scheme.展开更多
The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color...The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.展开更多
The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(...The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 ...AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 subjects from the complete questionnaire cycle in the NHANES database from 2005 to 2008 were included.By exclusion criteria,8460 subjects were screened with 1676 myopia samples and 6784 control samples.Subsequently,baseline characteristics,association analyses,risk stratification analyses,and receive operating characteristic curve(ROC)were used to investigate the associations between covariates and myopia.Then,the causal relationship was explored in depth by MR analysis,and was estimated the reliability by sensitivity analyses and directionality tests.RESULTS:Baseline characteristics illustrated a significant difference between myopia and controls for both asthma and covariates(excluding gender;P<0.05).The results in all three models indicated that asthma was strongly associated with myopia and the effect on myopia was not significantly confounded by other covariates[model 3:odd ratio(OR)=1.31;95%CI=1.07-1.62;P=0.0133].The risk stratification analysis again verified that asthma remained strongly associated with myopia and was a risk factor for myopia(P<0.05,OR>1).ROC proved that the model was accurate in its prediction[area under curve(AUC)=0.7].Subsequently,the causal relationship between them was statistically significant(P<0.05)according to the inverse variance weighted(IVW)method in MR.Scatterplot showed that asthma and myopia had significant positive causality and were not affected by confounders.Forest plot displayed an increasing risk of myopia on asthma(OR>1).The funnel plot demonstrated compliance with Mendel’s second law.Sensitivity analysis and directional analysis further confirmed the confidence of the MR analysis results and a unidirectional causal relationship between them.CONCLUSION:A significant association and causality between asthma and myopia is found through the NHANES database and MR analysis,which is important implications for public health policy development and clinical practice.展开更多
AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analy...AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.展开更多
There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixtu...There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixture affect mechanistic performance.At the same time,artificial intelligence(AI)-driven approaches are becoming more popular in analysing asphalt mixtures,yet there are limited comparisons of regression and machine learning(ML)models for mechanistic performance interpretation.Consequently,a comparison of AI and statistical approaches is presented in this study for predicting bituminous mixture properties such as stiffness,fatigue resistance,and tensile strength.Some of the important input features are bitumen content,crumb rubber content,and air void content.The research uses random forest model(RFM),linear regression model(LRM),and polynomial regression model(PRM).RFM and PRM achieved an R^(2) as high as 0.94,with mean absolute error(MAE)less than 2.5,and are,therefore,good predictive models.Interestingly,RFM works best in one-third of instances,particularly when dealing with outliers,whereas traditional statistical models work better in two-thirds of instances.The results highlight AI's value in bituminous mixture optimisation,where RFM showed good prediction accuracy.In 30%of the cases,AI models outperformed the conventional statistical approaches.At the same time,analyses show that model performance varies significantly with scenarios and that even if AI models capture complex nonlinear relationships,they must not override DOE principles.展开更多
基金funded by the National Natural Science Foundation of China(No.82273704)Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0501400-2023ZD0501402)+4 种基金Beijing Hospitals Authority’s Ascent Plan(DFL20241102)Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support(No.ZLRK202325)China Postdoctoral Science Foundation(2024M760152)Peking University Medicine Fund for World’s Leading Discipline or Discipline Cluster Development(No.BMU2022XKQ004)Science Foundation of Peking University Cancer Hospital(Nos.BJCH2024BJ02,XKFZ2410,BJCH2025CZ04,and 2022-27)。
文摘Objective:Based on multistage metabolomic profiling and Mendelian randomization analyses,the current study identified plasma metabolites that predicted the risk of developing gastric cancer(GC)and determined whether key metabolite levels modified the GC primary prevention effects.Methods:Plasma metabolites associated with GC risk were identified through a case-control study.Bi-directional two-sample Mendelian randomization analyses were performed to determine potential causal relationships utilizing the Shandong Intervention Trial(SIT),a nested case-control study of the Mass Intervention Trial in Linqu,Shandong province(MITS),China,the UK Biobank,and the Finn Gen project.Results:A higher genetic risk score for plasma L-aspartic acid was significantly associated with an increased GC risk in the northern Chinese population(SIT:HR=1.26 per 1 SD change,95%CI:1.07±1.49;MITS:HR=1.07,95%CI:1.00±1.14)and an increased gastric adenocarcinoma risk in Finn Gen(OR=1.68,95%CI:1.16±2.45).Genetically predicted plasma L-aspartic acid levels also modified the GC primary prevention effects with the beneficial effect of Helicobacter pylori eradication notably observed among individuals within the top quartile of L-aspartic acid level(P-interaction=0.098)and the beneficial effect of garlic supplementation only for those within the lowest quartile of L-aspartic acid level(P-interaction=0.02).Conclusions:Elevated plasma L-aspartic acid levels significantly increased the risk of developing GC and modified the effects of GC primary prevention.Further studies from other populations are warranted to validate the modification effect of plasma L-aspartic acid levels on GC prevention and to elucidate the underlying mechanisms.
文摘To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.
基金supported by the NSFC(12271141)supported by the Fundamental Research Funds for the Central Universities(B240205026)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX24_0821).
文摘In this paper, we consider the existence of pullback random exponential attractor for non-autonomous random reaction-diffusion equation driven by nonlinear colored noise defined onR^(N) . The key steps of the proof are the tails estimate and to demonstrate the Lipschitz continuity and random squeezing property of the solution for the equation defined on R^(N) .
基金supported by the China State Railway Group Co.,Ltd.Science and Technology Research and Development Program Project(Grant No.L2024G007)the Natural Science Foundation of Hunan Province(Grant No.2024JJ5427)+1 种基金the National Natural Science Foundation of China(Grant No.52478321,52078485)the Science and Technology Research and Development Program Project of China Railway Group Limited(Grant No.2021-Special-08,2022-Key-06&2023-Key-22).
文摘To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.
基金supported by the Science Foundation of Zhejiang Province of China(Grant No.LY22E080016)the National Natural Science Foundation of China(Grant No.51808499)the Fundamental Research Funds of Zhejiang Sci-Tech University(Grant No.24052126-Y).
文摘Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.
基金supported by the National Key Research and Development Program of China(2021YFB3301200)the National Natural Science Foundation of China(NSFC)(U21A20483,62373040,62203042).
文摘Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.
基金National Natural Science Foundation of China(Grant No.42274180)National Key Research and Development Program of China(2021YFC2902003).
文摘Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.
文摘In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.
文摘Objective:To evaluate the impact of subcutaneous tunneling on peripherally inserted central catheters(PICCs)dislodgement and malposition.Dislodged or malpositioned PICCs can lead to improper treatment.The subcutaneous tunneling strategy may be effective,but there is insufficient evidence,and proximal movement has not been explored.Methods:We randomized 630 patients who needed PICCs placement to either the tunneled PICCs(experimental group)or the non-tunneled PICCs(control group).Dislodgement and malposition of the catheter were the primary outcomes,and catheter-related infection(CRI)and catheter-related thrombosis(CRT)were the secondary outcomes.Results:Subcutaneous tunneling does not significantly reduce distal catheter movement,but it significantly reduces proximal catheter movement(4.3%vs.9.9%,P=0.007),which may explain the lower incidence of CRI(2.0%vs.5.3%,P=0.030)and CRT(3.6%vs.12.5%,P<0.001).Conclusions:Although subcutaneous tunneling does not significantly improve catheter prolapse,it should still be used clinically because proximal catheter movement can be a more serious problem associated with CRI and CRT.
基金National Natural Science Foundation of China:Study on the Mechanism of Qingre jiedu Huatan Formula Regulating Macrophage Mitochondrial Autophagy/NLR family pyrin domain containing 3 Inflammasome Activation and Intervening intestinal-Lung Bacterial Translocation in the Treatment of Severe Pneumonia(No.82074411)Science and Technology Innovation Team Support Program of Universities in Henan Province:Study on Prevention and Treatment of Respiratory Critical Illness with Traditional Chinese Medicine(No.22IRTSTHN029)Traditional Chinese Medicine Discipline Construction Project of Characteristic Backbone Discipline in Henan Province:Integrated Traditional Chinese and Conventional Medicine in Treatment of Severe Novel Coronavirus Infection:a Randomized Controlled Trial(No.STG-ZYX02-202204)。
文摘OBJECTIVE:To assess the effect of Traditional Chinese Medicine(TCM)treatment with syndrome differentiation in patients with severe community-acquired pneumonia(CAP).METHODS:A multicenter,randomized,placebocontrolled trial was conducted.Adult patients with severe CAP were randomly allocated to receive conventional medicine treatment(conventional group)or conventional medicine combine with TCM treatment with syndrome differentiation(combination group)underwent a 28-d treatment time.The primary endpoint was treatment failure.Secondary outcomes included time to clinical stability,28-d mortality,90-d mortality,length of hospital stay.RESULTS:A total of 183 patients were included in the intention-to-treat(ITT)population,with 91 allocated to the combination group and 92 to the conventional group.Patients with treatment failure in the combination group(18/91,19.8%)were lower compared with the conventional group(34/92,37.0%).Time to clinical stability was shorter in the combination group(11.8 d)than in the conventional group[17.8 d;HR=2.08,95%CI(1.38,3.14);P=0.001].The 28-d mortality was lower in the combination group(17.6%)than in the conventional group(31.5%).There was no difference in the length of hospital stay between the two groups(P=0.901).Adverse events were evenly distributed between the combination and conventional groups(P=0.837).CONCLUSION:Among patients with severe CAP,TCM treatment with syndrome differentiation reduced treatment failure,time to clinical stability,and the 28-d mortality and the 90-d mortality among patients admitted to the ward,without an increase in complications.
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
基金the financial support from the National Key R&D Program of China(No.2022YFC2904405)the National Natural Science Foundation of China(Nos.22078055,51774079)。
文摘To synergistically recover alumina and alkali from red mud(RM),the structural stability and conversion mechanism of hydroandradite(HA)from hydrogarnet(HG)were investigated via the First-principles,XRF,XRD,PSD and SEM methods,and a novel hydrothermal process based on the conversion principle was finally proposed.The crystal structure simulation shows that the HA with varied silicon saturation coefficients is more stable than HG,and the HA with a high iron substitution coefficient is more difficult to be converted from HG.The(110)plane of Fe_(2)O_(3) is easier to combine with HG to form HA,and the binding energy is 81.93 kJ/mol.The effects of raw material ratio,solution concentration and hydrothermal parameters on the conversion from HG to HA were revealed,and the optimal conditions for the alumina recovery were obtained.The recovery efficiencies of alumina and Na_(2)O from the RM are 63.06%and 97.34%,respectively,and the Na_(2)O content in the treated RM is only 0.13%.
文摘In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and multiple user detection techniques,our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings,where the upper bound on performance is 1.Moreover,a lower bound on throughput performance is derived,which is tight in some parameter settings and can be used to approximate theoretical performance.Simulation results validate our analysis and confirm the advantages of our proposed scheme.
基金supported by the Key R&D Program of Shaanxi Province,China(2024NC-YBXM-146)the Xi’an Agricultural Technology Research and Development Project,China(24NYGG0048)+1 种基金the Key R&D Program of Xianyang,China(L2024-ZDYF-ZDYF-NY-0028)the National Foreign Expert Project of China(G2023172002L)。
文摘The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.
基金support in providing the data and the Universiti Teknologi Malaysia supported this work under UTM Flagship CoE/RG-Coe/RG 5.2:Evaluating Surface PGA with Global Ground Motion Site Response Analyses for the highest seismic activity location in Peninsular Malaysia(Q.J130000.5022.10G47)Universiti Teknologi Malaysia-Earthquake Hazard Assessment in Peninsular Malaysia Using Probabilistic Seismic Hazard Analysis(PSHA)Method(Q.J130000.21A2.06E9).
文摘The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
基金Supported by the Hainan Provincial Natural Science Foundation of China(No.825RC898)Hainan Province Clinical Medical Center。
文摘AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 subjects from the complete questionnaire cycle in the NHANES database from 2005 to 2008 were included.By exclusion criteria,8460 subjects were screened with 1676 myopia samples and 6784 control samples.Subsequently,baseline characteristics,association analyses,risk stratification analyses,and receive operating characteristic curve(ROC)were used to investigate the associations between covariates and myopia.Then,the causal relationship was explored in depth by MR analysis,and was estimated the reliability by sensitivity analyses and directionality tests.RESULTS:Baseline characteristics illustrated a significant difference between myopia and controls for both asthma and covariates(excluding gender;P<0.05).The results in all three models indicated that asthma was strongly associated with myopia and the effect on myopia was not significantly confounded by other covariates[model 3:odd ratio(OR)=1.31;95%CI=1.07-1.62;P=0.0133].The risk stratification analysis again verified that asthma remained strongly associated with myopia and was a risk factor for myopia(P<0.05,OR>1).ROC proved that the model was accurate in its prediction[area under curve(AUC)=0.7].Subsequently,the causal relationship between them was statistically significant(P<0.05)according to the inverse variance weighted(IVW)method in MR.Scatterplot showed that asthma and myopia had significant positive causality and were not affected by confounders.Forest plot displayed an increasing risk of myopia on asthma(OR>1).The funnel plot demonstrated compliance with Mendel’s second law.Sensitivity analysis and directional analysis further confirmed the confidence of the MR analysis results and a unidirectional causal relationship between them.CONCLUSION:A significant association and causality between asthma and myopia is found through the NHANES database and MR analysis,which is important implications for public health policy development and clinical practice.
基金Supported by the Central High Level Hospital Clinical Research Funding(No.BJ-2024-089).
文摘AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.
基金sustained them with this research(including Eng.Giuseppe Colicchio)and the European Commission for its financial contribution to the LIFE SILENT project“Sustainable Innovations for Long-life Environmental Noise Technologies”(LIFE22-ENV-IT-LIFE-SILENT/101114310.Acronym:LIFE22-ENV-ITLIFE SILENT)the LIFE SNEAK Project“Optimised Surfaces Against Noise and Vibrations Produced by Tramway Track and Road Traffic”(LIFE20 ENV/IT/000181.Acronym:LIFE SNEAK).
文摘There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixture affect mechanistic performance.At the same time,artificial intelligence(AI)-driven approaches are becoming more popular in analysing asphalt mixtures,yet there are limited comparisons of regression and machine learning(ML)models for mechanistic performance interpretation.Consequently,a comparison of AI and statistical approaches is presented in this study for predicting bituminous mixture properties such as stiffness,fatigue resistance,and tensile strength.Some of the important input features are bitumen content,crumb rubber content,and air void content.The research uses random forest model(RFM),linear regression model(LRM),and polynomial regression model(PRM).RFM and PRM achieved an R^(2) as high as 0.94,with mean absolute error(MAE)less than 2.5,and are,therefore,good predictive models.Interestingly,RFM works best in one-third of instances,particularly when dealing with outliers,whereas traditional statistical models work better in two-thirds of instances.The results highlight AI's value in bituminous mixture optimisation,where RFM showed good prediction accuracy.In 30%of the cases,AI models outperformed the conventional statistical approaches.At the same time,analyses show that model performance varies significantly with scenarios and that even if AI models capture complex nonlinear relationships,they must not override DOE principles.
