In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the ...In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the influence of input uncertainties on multiple responses in an engineering system.The sensitivity indices are determined by comparing a reference model that incorporates all system uncertainties,with an altered model,where specific uncertainties are constrained.The proposed framework employs probability distance measures such as Hellinger distance,Kullback-Leibler divergence,and I2 norm which are based on joint probability density functions.The study also demonstrates the equivalence between the l2 norm-based approach and Sobol's analysis in multivariate sensitivity context.The proposed methodology effectively manages correlated random variables,accommodates both Gaussian and non-Gaussian distributions,and allows for the grouping of input variables.Ilustrative examples consist of static analysis of a truss system and dynamic analysis of a frame subjected to seismic excitation.The sensitivity indices are estimated using brute-force Monte Carlo simulations.The relative ranking of these sensitivity indices can be utilized to identify the most and least significant variables contributing to the response uncertainty.The numerical results show a consistent ranking of input variables across different probability measures,indicating the robustness of proposed framework.展开更多
[Objectives]To study the factors influencing the tourism economy in Henan Province.[Methods]Using tourism-related data from Henan Province covering the period from 2000 to 2020,this study constructs a regression model...[Objectives]To study the factors influencing the tourism economy in Henan Province.[Methods]Using tourism-related data from Henan Province covering the period from 2000 to 2020,this study constructs a regression model based on multivariate statistical methods to investigate the determinants of the tourism economy.The dependent variable in the model is the domestic tourism revenue of Henan Province,while the independent variables comprise the number of tourist arrivals,total operational railway mileage,the number of travel agencies,and the per capita disposable income of urban residents.[Results]Both the total railway mileage and the per capita disposable income of urban residents are the primary factors influencing the development of Henan's tourism economy.[Conclusions]It is recommended to reduce uncertainty and liquidity constraints to mitigate residents'precautionary savings behavior,actively expand domestic demand to leverage tourism as an economic driver,and improve infrastructure to support tourism development.展开更多
The One Health concept acknowledges the importance of multiple dimensions in controlling antimicrobial resistance(AMR).However,our understanding of how anthropological,socioeconomic,and environmental factors drive AMR...The One Health concept acknowledges the importance of multiple dimensions in controlling antimicrobial resistance(AMR).However,our understanding of how anthropological,socioeconomic,and environmental factors drive AMR at a national level remains limited.To explore associations between potential contributing factors and AMR,this study analyzed an extensive database comprising 13 major antibioticresistant bacteria and over 30 predictors(e.g.,air pollution,antibiotic usage,economy,husbandry,public services,health services,education,diet,climate,and population)from 2014 to 2020 across China.The multivariate analysis results indicate that fine particulate matter with a diameter of 2.5 μm or less(PM_(2.5))is associated with AMR,accounting for 12%of the variation,followed by residents’income(10.3%)and antibiotic usage density(5.1%).A reduction in PM_(2.5)of 1 μg·m^(-3)is linked to a 0.17%decrease in aggregate antibiotic resistance(p<0.001,R^(2)=0.74).Under different scenarios of China’s PM_(2.5)airquality projections,we further estimated the premature death toll and economic burden derived from PM_(2.5)-related antibiotic resistance in China until 2060.PM_(2.5)-derived AMR is estimated to cause approximately 27000(95%confidence interval(CI):646848830)premature deaths and about 0.51(95%CI;0.12-0.92)million years of life lost annually in China,equivalent to an annual welfare loss of 8.4(95%CI;2.0-15.0)billion USD.Implementing the“Ambitious Pollution 1.5℃ Goals”scenario to reduce PM_(2.5)concentrations could prevent roughly 14000(95%CI;3324-26320)premature deaths—with a potential monetary value of 9.8(95%CI;2.2-17.6)billion USD—from AMR by 2060.These results suggest that reducing air pollution may offer co-benefits in the health and economic sectors by mitigating AMR.展开更多
In this article,the dynamical model and trajectory tracking problem for a tilt-rotor unmanned aerial vehicle is tackled through linear Active Disturbance Rejection Control(ADRC)applied on the tangent linearized system...In this article,the dynamical model and trajectory tracking problem for a tilt-rotor unmanned aerial vehicle is tackled through linear Active Disturbance Rejection Control(ADRC)applied on the tangent linearized system.To apply the ADRC scheme,it is considered the subsystem without the Y-axis component,which is differentially flat and whose flat outputs are obtained using the Kronecker matrix.Numerical assessment using as system parameters the ones of a scale prototype is provided to show the effectiveness of the proposal leading to accurate tracking results using admissible control values for an experimental scenario.展开更多
Multivariate time series(MTS)data are vital for various applications,particularly in machine learning tasks.However,challenges such as sensor failures can result in irregular and misaligned data with missing values,th...Multivariate time series(MTS)data are vital for various applications,particularly in machine learning tasks.However,challenges such as sensor failures can result in irregular and misaligned data with missing values,thereby complicating their analysis.While recent advancements use graph neural networks(GNNs)to manage these Irregular Multivariate Time Series(IMTS)data,they generally require a reliable graph structure,either pre-existing or inferred from adequate data to properly capture node correlations.This poses a challenge in applications where IMTS data are often streamed and waiting for future data to estimate a suitable graph structure becomes impractical.To overcome this,we introduce a dynamic GNN model suited for streaming characteristics of IMTS data,incorporating an instance-attention mechanism that dynamically learns and updates graph edge weights for real-time analysis.We also tailor strategies for high-frequency and low-frequency data to enhance prediction accuracy.Empirical results on real-world datasets demonstrate the superiority of our proposed model in both classification and imputation tasks.展开更多
Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements ...Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements of statistical methods due to the closure effect.In this study,we applied an integrated approach combining compositional data,multifractal,and multivariate statistical analyses to identify the nonlinear complexity of the spatial distributions of elemental concentrations in the Er’renshan ore field.Initially,the raw concentrations were transformed into log-ratios following the principles of composition data theory to alleviate the impact of the closure effect.Multifractal analysis was then conducted to characterise the nonlinear complexity of the concentration distributions.Furthermore,principal component analysis(PCA)and factor analysis(FA)were applied to identify spurious correlations and the potential factors controlling the distribution patterns.The results demonstrate that:a)the raw data are biased,while the log-ratio data are unbiased and more reliable;b)the spatial distributions of elemental concentrations exhibit nonlinear complexity;and c)the elemental distribution in the study area is largely controlled by structural factors.展开更多
Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and...Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.展开更多
The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate li...The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA.A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology,Jiangsu Province Hospital of Chinese Medicine(Nanjing,China),aged 18 to 60 years.General demographic information,medical history,and laboratory results were collected to assess metabolic disturbances.Sexual function was evaluated using the 5-item version of the International Index of Erectile Function(IIEF-5)questionnaire.Based on univariate analysis,variables associated with IIEF-5 scores were identified,and the correlations between them were evaluated.The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models.Fasting plasma glucose(β=−0.628,P<0.001),uric acid(β=−0.552,P<0.001),triglycerides(β=−0.088,P=0.047),low-density lipoprotein cholesterol(β=−0.164,P=0.027),glycated hemoglobin(HbA1c;β=−0.562,P=0.012),and smoking history(β=−0.074,P=0.037)exhibited significant negative impacts on erectile function.The coefficient of determination(R²)for the model was 0.239,and the adjusted R²was 0.230,indicating overall statistical significance(F-statistic=26.52,P<0.001).Metabolic parameters play a crucial role in the development of ED.Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.展开更多
The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for proce...The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for processing sound locations.The neural activation in regions along this pathway encodes sound locations by opponent hemifield coding,in which each unilateral region is activated by sounds coming from the contralateral hemifield.However,it is still unclear how these regions interact with each other to form a unified representation of the auditory space.In the present study,we investigated whether functional connectivity in the auditory“where”pathway encoded sound locations during passive listening.Participants underwent functional magnetic resonance imaging while passively listening to sounds from five distinct horizontal locations(−90°,−45°,0°,45°,90°).We were able to decode sound locations from the functional connectivity patterns of the“where”pathway.Furthermore,we found that such neural representation of sound locations was primarily based on the coding of sound lateralization angles to the frontal midline.In addition,whole-brain analysis indicated that functional connectivity between occipital regions and the primary auditory cortex also encoded sound locations by lateralization angles.Overall,our results reveal a lateralization-angle-based representation of sound locations encoded by functional connectivity patterns,which could add on the activation-based opponent hemifield coding to provide a more precise representation of the auditory space.展开更多
The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide dist...The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.展开更多
In 2023,a multivariate normality test based on a chi-square approximation was developed.This method assumes independence among Gaussian random variables,and defines the test statistic,denoted by Q,as the sum of square...In 2023,a multivariate normality test based on a chi-square approximation was developed.This method assumes independence among Gaussian random variables,and defines the test statistic,denoted by Q,as the sum of squared values.This study aims to develop R scripts that implement the Q-test for mul-tivariate normality using either the Shapiro-Wilk W statistic(QSWa)or the Shapiro-Francia W’statistic(QSFa).A bootstrap version of the Q-test(QSWb and QSFb),which does not assume independence,is also included.Addition-ally,it incorporates Royston’s H-test.The use of the scripts is illustrated with a sample of 50 participants assessed on a variable across four yearly admin-istrations.The sampling distribution generated by the bootstrap method dif-fers from the chi-square distribution and corresponds to a generalized chisquare distribution-namely,the distribution of a sum of squares of correlated variables.This distribution is less peaked and has a heavier right tail than the chi-square distribution.It is concluded that the bootstrap approach is con-servative under the null hypothesis of multivariate normality;however,it is theoretically more appropriate than the chi-square approximation.To ap-proximate the distributions of the two versions of the Q-test,it is recom-mended that the z or z’values set to zero in the calculation of the Q statistic not be subtracted when determining the degrees of freedom in the chi-square approximation.Moreover,a significance level of 10%is suggested for the bootstrap approach,rather than the conventional 5%.展开更多
In this study,we used an extensive sampling network established in central Romania to develop tree height and crown length models.Our analysis included more than 18,000 tree measurements from five different species.In...In this study,we used an extensive sampling network established in central Romania to develop tree height and crown length models.Our analysis included more than 18,000 tree measurements from five different species.Instead of building univariate models for each response variable,we employed a multivariate approach using seemingly unrelated mixed-effects models.These models incorporated variables related to species mixture,tree and stand size,competition,and stand structure.With the inclusion of additional variables in the multivariate seemingly unrelated mixed-effects models,the accuracy of the height prediction models improved by over 10% for all species,whereas the improvement in the crown length models was considerably smaller.Our findings indicate that trees in mixed stands tend to have shorter heights but longer crowns than those in pure stands.We also observed that trees in homogeneous stand structures have shorter crown lengths than those in heterogeneous stands.By employing a multivariate mixed-effects modelling framework,we were able to perform cross-model random-effect predictions,leading to a significant increase in accuracy when both responses were used to calibrate the model.In contrast,the improvement in accuracy was marginal when only height was used for calibration.We demonstrate how multivariate mixed-effects models can be effectively used to develop multi-response allometric models that can be easily calibrated with a limited number of observations while simultaneously achieving better-aligned projections.展开更多
OBJECTIVE:To find more influencing factors Qideficiency constitution of Traditional Chinese Medicine(TCM)using dynamic and comprehensive information.METHODS:Because grey relational analysis(GRA)is good at processing i...OBJECTIVE:To find more influencing factors Qideficiency constitution of Traditional Chinese Medicine(TCM)using dynamic and comprehensive information.METHODS:Because grey relational analysis(GRA)is good at processing incomplete information and has no special requirements for sample size and distribution.We acquired 2122 pieces of valid Qi-deficiency constitution dynamic data after preprocessing,and used GRA combing withχ~2 test and multivariate logistic regression analysis to discover and sort the influencing factors of Qideficiency constitution.RESULTS:For the calculation results of GRA,there were 10(62.5%)aspects whose grey correlation degrees were greater than 0.6.The results ofχ~2 test showed that all the above 10 aspects were statistically significant with Qideficiency constitution.The analysis results of multivariate logistic regression analysis showed the following factors were positively correlated with Qi-deficiency constitution:premature birth,sleeping late and getting up early,sleeping late and getting up late,irregular sleeping,sleeping 6.0-6.9 h per day,artificial feeding,female,age at 18-19,and father's age at 18-19 years old when a baby at birth.The following factors were negatively correlated with Qi-deficiency constitution:sleeping 8.0-8.9 h per day and≥9.0 h per day,and age at 30-39 and 40-49 years old.CONCLUSIONS:It is necessary to pay attention to these innate and acquired information of individuals which may lead to Qi-deficiency constitution.And our research also provides a novel methodological thinking for analyzing the influential factors of TCM constitution.展开更多
In a previous article,an R script was developed and divided into three parts to implement the multivariate normality(MVN)Q-test based on both the chisquare approximation and the bootstrap approach,using either the Sha...In a previous article,an R script was developed and divided into three parts to implement the multivariate normality(MVN)Q-test based on both the chisquare approximation and the bootstrap approach,using either the Shapiro-Wilk W statistic(QSWa and QSWb)or the Shapiro-Francia W’statistic(QSFa and QSFb).Royston’s H-test was included as a supplementary MVN test.The aim of this study is to compare the hit rate and statistical power of the four Qtest variants and the H-test using 200 samples drawn from multivariate stand-ard normal distributions and 200 samples from multivariate t-distributions with five degrees of freedom.The simulations vary in sample size(50,75,100,125,150,200,250,and 500),number of variables(from 2 to 6),and homoge-neous inter-variable correlation(0,0.3,0.5,0.7,and 0.9).The H-test outper-formed QSWb and QSFb,but not QSWa in the multivariate normal samples or QSFa in the multivariate t-distribution samples.QSFb performed better than QSWb.It is concluded that the bootstrap approach is conservative under the null hypothesis of multivariate normality.However,when the assumption of independence is violated,the bootstrap approach is theoretically more ap-propriate than QSWa and QSFa.A 10%significance level is recommended for QSFb in terms of hit rate,but in terms of statistical power,only when rejecting the null hypothesis.展开更多
The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmente...The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.展开更多
China has achieved the poverty reduction goal of the United Nations 2030 Agenda for Sustainable Development 10 years ahead of schedule,contributing significantly to global poverty reduction.Despite extended efforts in...China has achieved the poverty reduction goal of the United Nations 2030 Agenda for Sustainable Development 10 years ahead of schedule,contributing significantly to global poverty reduction.Despite extended efforts in poverty elimination,there is a lack of quantitative studies categorizing and comparing poverty-elimination counties(PECs)based on their processes.This study proposes an innovative framework for analyzing PECs’development paths from the perspective of population-land-industry(PLI).We quantify the PLI matching degree of PECs in China during the critical phase of the battle against poverty through a multivariate matching model,classify PECs via K-means clustering according to the consistency in PLI matching degree evolution,and summarize the typical development patterns of PECs.Results indicate that the PLI matching degree of PECs in China increased substantially from 2015 to 2020,particularly in eastern areas,while the western region,including the Qinghai-Xizang Plateau and southwestern Xinjiang,shows untapped potential for improvement.Five types of PECs are identified,with the majority(30.1%)showing sustained moderate PLI matching and a minority(9.6%)experiencing long-term PLI mismatch.Industry is the shortfall of various PECs,and effective strategies to facilitate all types of PECs include the development of emerging businesses and the expansion of secondary and tertiary industries.Additionally,enriching rural labor force and increasing farmland use efficiency are essential for optimal PLI matching and positive interaction,ultimately ensuring poverty elimination and sustainable development.展开更多
The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of...The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.展开更多
The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first dis...The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.展开更多
文摘In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the influence of input uncertainties on multiple responses in an engineering system.The sensitivity indices are determined by comparing a reference model that incorporates all system uncertainties,with an altered model,where specific uncertainties are constrained.The proposed framework employs probability distance measures such as Hellinger distance,Kullback-Leibler divergence,and I2 norm which are based on joint probability density functions.The study also demonstrates the equivalence between the l2 norm-based approach and Sobol's analysis in multivariate sensitivity context.The proposed methodology effectively manages correlated random variables,accommodates both Gaussian and non-Gaussian distributions,and allows for the grouping of input variables.Ilustrative examples consist of static analysis of a truss system and dynamic analysis of a frame subjected to seismic excitation.The sensitivity indices are estimated using brute-force Monte Carlo simulations.The relative ranking of these sensitivity indices can be utilized to identify the most and least significant variables contributing to the response uncertainty.The numerical results show a consistent ranking of input variables across different probability measures,indicating the robustness of proposed framework.
文摘[Objectives]To study the factors influencing the tourism economy in Henan Province.[Methods]Using tourism-related data from Henan Province covering the period from 2000 to 2020,this study constructs a regression model based on multivariate statistical methods to investigate the determinants of the tourism economy.The dependent variable in the model is the domestic tourism revenue of Henan Province,while the independent variables comprise the number of tourist arrivals,total operational railway mileage,the number of travel agencies,and the per capita disposable income of urban residents.[Results]Both the total railway mileage and the per capita disposable income of urban residents are the primary factors influencing the development of Henan's tourism economy.[Conclusions]It is recommended to reduce uncertainty and liquidity constraints to mitigate residents'precautionary savings behavior,actively expand domestic demand to leverage tourism as an economic driver,and improve infrastructure to support tourism development.
基金funded by the National Natural Science Foun-dation of China(22406168,W2411031,and 52270201)the China Postdoctoral Science Foundation(2023M733061)the Zhejiang University Global Partnership Fund(100000-11320/198).
文摘The One Health concept acknowledges the importance of multiple dimensions in controlling antimicrobial resistance(AMR).However,our understanding of how anthropological,socioeconomic,and environmental factors drive AMR at a national level remains limited.To explore associations between potential contributing factors and AMR,this study analyzed an extensive database comprising 13 major antibioticresistant bacteria and over 30 predictors(e.g.,air pollution,antibiotic usage,economy,husbandry,public services,health services,education,diet,climate,and population)from 2014 to 2020 across China.The multivariate analysis results indicate that fine particulate matter with a diameter of 2.5 μm or less(PM_(2.5))is associated with AMR,accounting for 12%of the variation,followed by residents’income(10.3%)and antibiotic usage density(5.1%).A reduction in PM_(2.5)of 1 μg·m^(-3)is linked to a 0.17%decrease in aggregate antibiotic resistance(p<0.001,R^(2)=0.74).Under different scenarios of China’s PM_(2.5)airquality projections,we further estimated the premature death toll and economic burden derived from PM_(2.5)-related antibiotic resistance in China until 2060.PM_(2.5)-derived AMR is estimated to cause approximately 27000(95%confidence interval(CI):646848830)premature deaths and about 0.51(95%CI;0.12-0.92)million years of life lost annually in China,equivalent to an annual welfare loss of 8.4(95%CI;2.0-15.0)billion USD.Implementing the“Ambitious Pollution 1.5℃ Goals”scenario to reduce PM_(2.5)concentrations could prevent roughly 14000(95%CI;3324-26320)premature deaths—with a potential monetary value of 9.8(95%CI;2.2-17.6)billion USD—from AMR by 2060.These results suggest that reducing air pollution may offer co-benefits in the health and economic sectors by mitigating AMR.
基金supported by the Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional(SIP-IPN)under grants 20250098,20250168,20251291 and 20254791by Universidad Iberoamericana Ciudad de México+1 种基金Victor G.Sánchez-Meza is a Secretaría de Ciencia,Humanidades,Tecnología e Innovación(Secihti)fellow(CVU 964590)support received.Yair Lozano thanks the support of the Coordinación de Operación y Redes de Investigación y Posgrado of the IPN through the Red de Inteligencia Artificial y Ciencia de Datos and the Red de Expertos en Innovación Automotriz.
文摘In this article,the dynamical model and trajectory tracking problem for a tilt-rotor unmanned aerial vehicle is tackled through linear Active Disturbance Rejection Control(ADRC)applied on the tangent linearized system.To apply the ADRC scheme,it is considered the subsystem without the Y-axis component,which is differentially flat and whose flat outputs are obtained using the Kronecker matrix.Numerical assessment using as system parameters the ones of a scale prototype is provided to show the effectiveness of the proposal leading to accurate tracking results using admissible control values for an experimental scenario.
基金supported by the UoA Start-up Grant,UQ Cyber Security Seed Funding,the Australian Research Council Linkage Project(LP230200821)the Australian Research Council Early Career Industry Fellowship(IE240100275)+1 种基金the Australian Research Council Discovery Project(DP240103070)the Australian Research Council Discovery Early Career Researcher Award(DE230101116).
文摘Multivariate time series(MTS)data are vital for various applications,particularly in machine learning tasks.However,challenges such as sensor failures can result in irregular and misaligned data with missing values,thereby complicating their analysis.While recent advancements use graph neural networks(GNNs)to manage these Irregular Multivariate Time Series(IMTS)data,they generally require a reliable graph structure,either pre-existing or inferred from adequate data to properly capture node correlations.This poses a challenge in applications where IMTS data are often streamed and waiting for future data to estimate a suitable graph structure becomes impractical.To overcome this,we introduce a dynamic GNN model suited for streaming characteristics of IMTS data,incorporating an instance-attention mechanism that dynamically learns and updates graph edge weights for real-time analysis.We also tailor strategies for high-frequency and low-frequency data to enhance prediction accuracy.Empirical results on real-world datasets demonstrate the superiority of our proposed model in both classification and imputation tasks.
基金supported by the Doctoral Research Start-up Fund,East China University of Technology(DHBK2019313)the Open Research Fund Program of Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),the Ministry of Education(2020YSJS10)+1 种基金the Open Research Fund Program of Shandong Provincial Engineering Laboratory of Application and Development of Big Data for Deep Gold Exploration(SDK202224)the Basic Scientific Research Fund of the Institute of Geophysical and Geochemical Exploration,Chinese Academy of Geological Sciences(AS2022P03).
文摘Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements of statistical methods due to the closure effect.In this study,we applied an integrated approach combining compositional data,multifractal,and multivariate statistical analyses to identify the nonlinear complexity of the spatial distributions of elemental concentrations in the Er’renshan ore field.Initially,the raw concentrations were transformed into log-ratios following the principles of composition data theory to alleviate the impact of the closure effect.Multifractal analysis was then conducted to characterise the nonlinear complexity of the concentration distributions.Furthermore,principal component analysis(PCA)and factor analysis(FA)were applied to identify spurious correlations and the potential factors controlling the distribution patterns.The results demonstrate that:a)the raw data are biased,while the log-ratio data are unbiased and more reliable;b)the spatial distributions of elemental concentrations exhibit nonlinear complexity;and c)the elemental distribution in the study area is largely controlled by structural factors.
文摘Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.
基金supported by Jiangsu Provincial Science and Technology Plan Special Project(No.BK20231379)Key Project of Jiangsu Provincial Health Commission(No.ZDA2020025)+1 种基金Jiangsu Traditional Chinese Medicine Science and Technology Development Plan Project(No.MS2022023)Excellent Young Doctor Training Program of Jiangsu Province Hospital of Chinese Medicine(No.2023QB0126).
文摘The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA.A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology,Jiangsu Province Hospital of Chinese Medicine(Nanjing,China),aged 18 to 60 years.General demographic information,medical history,and laboratory results were collected to assess metabolic disturbances.Sexual function was evaluated using the 5-item version of the International Index of Erectile Function(IIEF-5)questionnaire.Based on univariate analysis,variables associated with IIEF-5 scores were identified,and the correlations between them were evaluated.The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models.Fasting plasma glucose(β=−0.628,P<0.001),uric acid(β=−0.552,P<0.001),triglycerides(β=−0.088,P=0.047),low-density lipoprotein cholesterol(β=−0.164,P=0.027),glycated hemoglobin(HbA1c;β=−0.562,P=0.012),and smoking history(β=−0.074,P=0.037)exhibited significant negative impacts on erectile function.The coefficient of determination(R²)for the model was 0.239,and the adjusted R²was 0.230,indicating overall statistical significance(F-statistic=26.52,P<0.001).Metabolic parameters play a crucial role in the development of ED.Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.
基金supported by the National Key Research and Development Program of China(2023YFF1203502)the National Natural Science Foundation of China(62171300,62301343,and 62394314)+1 种基金the Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203109)the Capital Medical University Research and Development Fund(PYZ22027).
文摘The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for processing sound locations.The neural activation in regions along this pathway encodes sound locations by opponent hemifield coding,in which each unilateral region is activated by sounds coming from the contralateral hemifield.However,it is still unclear how these regions interact with each other to form a unified representation of the auditory space.In the present study,we investigated whether functional connectivity in the auditory“where”pathway encoded sound locations during passive listening.Participants underwent functional magnetic resonance imaging while passively listening to sounds from five distinct horizontal locations(−90°,−45°,0°,45°,90°).We were able to decode sound locations from the functional connectivity patterns of the“where”pathway.Furthermore,we found that such neural representation of sound locations was primarily based on the coding of sound lateralization angles to the frontal midline.In addition,whole-brain analysis indicated that functional connectivity between occipital regions and the primary auditory cortex also encoded sound locations by lateralization angles.Overall,our results reveal a lateralization-angle-based representation of sound locations encoded by functional connectivity patterns,which could add on the activation-based opponent hemifield coding to provide a more precise representation of the auditory space.
基金CSIR for providing financial assistance(09/0420(11800)/2021EMR-I)。
文摘The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.
文摘In 2023,a multivariate normality test based on a chi-square approximation was developed.This method assumes independence among Gaussian random variables,and defines the test statistic,denoted by Q,as the sum of squared values.This study aims to develop R scripts that implement the Q-test for mul-tivariate normality using either the Shapiro-Wilk W statistic(QSWa)or the Shapiro-Francia W’statistic(QSFa).A bootstrap version of the Q-test(QSWb and QSFb),which does not assume independence,is also included.Addition-ally,it incorporates Royston’s H-test.The use of the scripts is illustrated with a sample of 50 participants assessed on a variable across four yearly admin-istrations.The sampling distribution generated by the bootstrap method dif-fers from the chi-square distribution and corresponds to a generalized chisquare distribution-namely,the distribution of a sum of squares of correlated variables.This distribution is less peaked and has a heavier right tail than the chi-square distribution.It is concluded that the bootstrap approach is con-servative under the null hypothesis of multivariate normality;however,it is theoretically more appropriate than the chi-square approximation.To ap-proximate the distributions of the two versions of the Q-test,it is recom-mended that the z or z’values set to zero in the calculation of the Q statistic not be subtracted when determining the degrees of freedom in the chi-square approximation.Moreover,a significance level of 10%is suggested for the bootstrap approach,rather than the conventional 5%.
基金supported by the European Union and the Romanian Government through the Competitiveness Operational Programme 2014–2020, under the project“Increasing the economic competitiveness of the forestry sector and the quality of life through knowledge transfer,technology and CDI skills”(CRESFORLIFE),ID P 40 380/105506, subsidiary contract no. 17/2020partially by the FORCLIMSOC Nucleu Programme (Contract 12N/2023)+2 种基金project PN 23090101CresPerfInst project (Contract 34PFE/December 30, 2021)“Increasing the institutional capacity and performance of INCDS ‘Marin Drǎcea’in RDI activities-CresPer”LM was financially supported by the Research Council of Finland's flagship ecosystem for Forest-Human-Machine Interplay–Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)(decision number 357909)
文摘In this study,we used an extensive sampling network established in central Romania to develop tree height and crown length models.Our analysis included more than 18,000 tree measurements from five different species.Instead of building univariate models for each response variable,we employed a multivariate approach using seemingly unrelated mixed-effects models.These models incorporated variables related to species mixture,tree and stand size,competition,and stand structure.With the inclusion of additional variables in the multivariate seemingly unrelated mixed-effects models,the accuracy of the height prediction models improved by over 10% for all species,whereas the improvement in the crown length models was considerably smaller.Our findings indicate that trees in mixed stands tend to have shorter heights but longer crowns than those in pure stands.We also observed that trees in homogeneous stand structures have shorter crown lengths than those in heterogeneous stands.By employing a multivariate mixed-effects modelling framework,we were able to perform cross-model random-effect predictions,leading to a significant increase in accuracy when both responses were used to calibrate the model.In contrast,the improvement in accuracy was marginal when only height was used for calibration.We demonstrate how multivariate mixed-effects models can be effectively used to develop multi-response allometric models that can be easily calibrated with a limited number of observations while simultaneously achieving better-aligned projections.
基金National Natural Science:Grey Correlation Analysis based on Dynamic Data to Construct A Knowledge Map of the Dynamic Change Law of Chinese Medicine Constitution(No.81904324)National Natural Science Foundation:Discovery and Algorithm Research of"Hierarchical Feature Association Network"for Subjective and Objective Symptoms of Diseases and Bioinformatics based on Data Lake Technology-Taking Diabetes as an Example(No.82174236)。
文摘OBJECTIVE:To find more influencing factors Qideficiency constitution of Traditional Chinese Medicine(TCM)using dynamic and comprehensive information.METHODS:Because grey relational analysis(GRA)is good at processing incomplete information and has no special requirements for sample size and distribution.We acquired 2122 pieces of valid Qi-deficiency constitution dynamic data after preprocessing,and used GRA combing withχ~2 test and multivariate logistic regression analysis to discover and sort the influencing factors of Qideficiency constitution.RESULTS:For the calculation results of GRA,there were 10(62.5%)aspects whose grey correlation degrees were greater than 0.6.The results ofχ~2 test showed that all the above 10 aspects were statistically significant with Qideficiency constitution.The analysis results of multivariate logistic regression analysis showed the following factors were positively correlated with Qi-deficiency constitution:premature birth,sleeping late and getting up early,sleeping late and getting up late,irregular sleeping,sleeping 6.0-6.9 h per day,artificial feeding,female,age at 18-19,and father's age at 18-19 years old when a baby at birth.The following factors were negatively correlated with Qi-deficiency constitution:sleeping 8.0-8.9 h per day and≥9.0 h per day,and age at 30-39 and 40-49 years old.CONCLUSIONS:It is necessary to pay attention to these innate and acquired information of individuals which may lead to Qi-deficiency constitution.And our research also provides a novel methodological thinking for analyzing the influential factors of TCM constitution.
文摘In a previous article,an R script was developed and divided into three parts to implement the multivariate normality(MVN)Q-test based on both the chisquare approximation and the bootstrap approach,using either the Shapiro-Wilk W statistic(QSWa and QSWb)or the Shapiro-Francia W’statistic(QSFa and QSFb).Royston’s H-test was included as a supplementary MVN test.The aim of this study is to compare the hit rate and statistical power of the four Qtest variants and the H-test using 200 samples drawn from multivariate stand-ard normal distributions and 200 samples from multivariate t-distributions with five degrees of freedom.The simulations vary in sample size(50,75,100,125,150,200,250,and 500),number of variables(from 2 to 6),and homoge-neous inter-variable correlation(0,0.3,0.5,0.7,and 0.9).The H-test outper-formed QSWb and QSFb,but not QSWa in the multivariate normal samples or QSFa in the multivariate t-distribution samples.QSFb performed better than QSWb.It is concluded that the bootstrap approach is conservative under the null hypothesis of multivariate normality.However,when the assumption of independence is violated,the bootstrap approach is theoretically more ap-propriate than QSWa and QSFa.A 10%significance level is recommended for QSFb in terms of hit rate,but in terms of statistical power,only when rejecting the null hypothesis.
基金the University of Shanghai for Science and Technology’s Medical Engineering Interdisciplinary Project(No.10-22-308-520)the Ministry of Education’s First Batch of Industry-Education Cooperation Collaborative Education Projects(No.202101042008)+1 种基金the Fundamental Research Funds for the Central Universities(No.YG2019QNA34)the Shanghai Municipal Health Commission for Youth Clinical Research Project(No.20194Y0134)。
文摘The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.
基金supported by the National Natural Science Foundation of China(Grants No.41931293,42271279,42293271,and 41801175).
文摘China has achieved the poverty reduction goal of the United Nations 2030 Agenda for Sustainable Development 10 years ahead of schedule,contributing significantly to global poverty reduction.Despite extended efforts in poverty elimination,there is a lack of quantitative studies categorizing and comparing poverty-elimination counties(PECs)based on their processes.This study proposes an innovative framework for analyzing PECs’development paths from the perspective of population-land-industry(PLI).We quantify the PLI matching degree of PECs in China during the critical phase of the battle against poverty through a multivariate matching model,classify PECs via K-means clustering according to the consistency in PLI matching degree evolution,and summarize the typical development patterns of PECs.Results indicate that the PLI matching degree of PECs in China increased substantially from 2015 to 2020,particularly in eastern areas,while the western region,including the Qinghai-Xizang Plateau and southwestern Xinjiang,shows untapped potential for improvement.Five types of PECs are identified,with the majority(30.1%)showing sustained moderate PLI matching and a minority(9.6%)experiencing long-term PLI mismatch.Industry is the shortfall of various PECs,and effective strategies to facilitate all types of PECs include the development of emerging businesses and the expansion of secondary and tertiary industries.Additionally,enriching rural labor force and increasing farmland use efficiency are essential for optimal PLI matching and positive interaction,ultimately ensuring poverty elimination and sustainable development.
基金supported in part by the National Natural Science Foundation of China(No.52372389)the Jiangsu Province Excellent Postdoctoral Program of China(No.2023ZB494)+1 种基金the Basic Research Program of Jiangsu Province,China(No.BK20241412)the National Science Foundation for Post-doctoral Scientists of China(No.2024M754131)。
文摘The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.
基金Supported by the National Natural Science Foundation of China(12271154)the Natural Science Foundation of Hunan Province(2022JJ30234)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20231032)。
文摘The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.
