To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio...The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.展开更多
Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competiti...Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.展开更多
Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exp...Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.展开更多
This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 f...This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 from twelve hot springs and were analyzed for physicochemical parameters(electric conductivity, p H,total dissolved solids, temperature, Ca, Mg, Na, K, HCO_3,Cl, SO_4, and SiO_2). The waters of the thermal springs have temperatures varying from 28 to 51 °C and electric conductivity values ranging from 853 to 5630 l S/cm. Q-mode Cluster analysis resulted in the determination of two major water types: a Ca–HCO_3–SO_4 type with a moderate salinity and a Na–K–Cl type with high salinity. The plot of the major ions versus the saturation indices suggested that the hydrogeochemistry of thermal groundwater is mainly controlled by dissolution/precipitation of carbonate minerals, dissolution of evaporite minerals(halite and gypsum), and ion exchange of Ca(and/or Mg) by Na. The Gibbs diagram shows that evaporation is another factor playing a minor role. Principal Component Analysis produced three significant factors which have 88.2% of totalvariance that illustrate the main processes controlling the chemistry of groundwaters, which are respectively: the dissolution of evaporite minerals(halite and gypsum), ion exchange, and dissolution/precipitation of carbonate minerals. The subsurface reservoir temperatures were calculated using different cation and silica geothermometers and gave temperatures ranging between 17 and 279 °C. The Na–K and Na–K-Ca geothermometers provided high temperatures(up to 279 °C), whereas, estimated geotemperatures from K/Mg geothermometers were the lowest(17–53 °C). Silica geothermometers gave the most reasonable temperature estimate of the subsurface waters overlap between 20 and 58 °C, which indicate possible mixing with cooler Mg groundwaters indicated by the Na–K–Mg plot in the immature water field and in silica and chloride mixing models. The results of stable isotope analyses(δ^(18) O and δ~2 H) suggest that the origin of thermal water recharge is precipitation, which recharged from a higher altitude(600–1200 m) and infiltrated through deep faults and fractures in carbonate formations. They circulate at an estimated depth that does not exceed 2 km and are heated by a high conductive heat flow before rising to the surface through faults that acted as hydrothermal conduits.During their ascent to the surface, they are subjected to various physical and chemical changes such as cooling by conduction and change in their chemical constituents due to the mixing with cold groundwaters.展开更多
This study evaluated the impact of chronicity (onset of injury to admission in-terval) on three domains of functional outcomes for a large group of traumatic brain injured (TBI) survivors. Subjects included 528 TBI ad...This study evaluated the impact of chronicity (onset of injury to admission in-terval) on three domains of functional outcomes for a large group of traumatic brain injured (TBI) survivors. Subjects included 528 TBI adults who were treated in post-hospital residential rehabilitation centers. Subjects were assigned to one of three chronicity groups: 1) Early Interval (EI), 2.00 - 8.00 months n = 245, 2) Mid Interval (MI), 8.01 - 24.00 months n = 129, and (3) Late Interval (LI), 24.01 months and greater n = 154. Functional status was assessed with the MPAI-4. RM MANCOVA was applied to evaluate differences among groups from admission to discharge. Rasch analysis demonstrated satisfactory construct validity and internal consistency (Person reliability = 0.90 - 0.94, Item reliability = 0.99) for the admission and discharge MPAI-4s. Controlling for LOS and age, the RM MANCOVA revealed that each chronicity group showed significant improvement in MPAI-4 abilities, adjustment, and participation indices from admission to discharge (p < 0.001). Improvement observed from admission to discharge was the greatest among the EI group (p < 0.001). This study demonstrated the utility of multivariate statistical approaches for understanding the complexities of TBI treatment outcomes. As measured across three domains of functioning, rehabilitation was effective in reducing disability for participants in each chronicity group. Of the three groups, EI participants presented as the most disabled at admission but also made the greatest gains when assessed at discharge.展开更多
This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and car...This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and cardiovascular disease (CVD) emergency room (ER) visits and chronic obstructive pulmonary disease (COPD) ER visits using multivariate geostatistics in Houston, Texas, from 2004 to 2009. Analyses showed lack of strong pair-wise association among the predictors of O3, PM2.5, wind speed, relative humidity, and temperature. Whereas CVD and COPD ER visits exhibited a strong positive correlation. Both outcomes drastically increased from 2006 possibly due to immigration from neighboring locations. Parametric testing showed that the data differed significantly between the years. Multivariate multiple regression results on the 2009 data showed that PM2.5, relative humidity, and temperature were significant to both CVD and COPD ER visits. Codispersion coefficients were constant which justified the assumption of intrinsic correlation. That is, our predictors had strong influence on the spatial variability of CVD and COPD ER visits. This multivariate geostatistics approach predicted an increase of 34% in CVD ER visits and 24% increase in COPD ER visits, which calls for more attention from policy makers. The use of multivariate geostatistics analyses enabled us to successfully detect the effects of risk factors on both outcomes.展开更多
Fatty acids(FAs) provide energy and also can be used to trace trophic relationships among organisms.Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months.We examined fatty acid...Fatty acids(FAs) provide energy and also can be used to trace trophic relationships among organisms.Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months.We examined fatty acid profiles in aestivated and non-aestivated A.japonicus using multivariate analyses(PERMANOVA,MDS,ANOSIM,and SIMPER).The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly.The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers.Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation,and long chain FAs such as eicosapentaenoic(EPA) and docosahexaenoic acid(DHA) that produced from intestinal degradation,are digested during deep aestivation.Specific changes in the fatty acid composition of A.japonicus during aestivation needs more detailed study in the future.展开更多
Surface sediment data acquired by the grab sampling technique were used in the present study to produce a high-resolution and full coverage surface grain-size mapping. The objective is to test whether the hypothetical...Surface sediment data acquired by the grab sampling technique were used in the present study to produce a high-resolution and full coverage surface grain-size mapping. The objective is to test whether the hypothetically natural relationship between the surface sediment distribution and complex bathymetry could be used to improve the quality of surface sediment patches mapping. This is based on our hypothesis that grain-size characteristics of the ridge surface sediments must be intrinsically related to the hydrodynamic condition, i.e. storm-induced currents and the geometry of the seabed morphology. The median grain-size data were obtained from grab samples with inclusive bathymetric point recorded at 713 locations on the high-energy and shallow shelf of the Spiekeroog Barrier Island at the German Bight of the Southern North Sea. The area features two-parallel shoreface-connected ridges which is situated obliquely WNW-SSE oriented and mostly sandy in texture. We made use the median grain-size (d50) as the predictand and the bathymetry as the covariable to produce a high-resolution raster map of median grain-size distribution using the Cokriging interpolation. From the cross-validation of the estimated median grain-size data with the measured ones, it is clear that the gradient of the linear regression line for Cokriging is leaning closer towards the theoretical perfect-correlation line (45°) compared to that for Anisotropy Kriging. The interpolation result with Cokriging shows more realistic estimates on the unknown points of the median grain-size and gave detail to surface sediment patchiness, which spatial scale is more or less in agreement with previous studies. In addition to the moderate correlation obtained from the Pearson correlation (r = 0.44), the cross-variogram shows a more precise nature of their spatial correlation, which is physically meaningful for the interpolation process. The present study partially contributes to the framework of habitat mapping and nature protection that is to fill the gaps in physical information in a high-energetic and shallow coastal shelf.展开更多
Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing...Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.展开更多
A regional groundwater quality evaluation was conducted in the deep Maastrichtian aquifer of Senegal through multivariate statistical analysis and a GIS-based water quality index using physicochemical data from 232 bo...A regional groundwater quality evaluation was conducted in the deep Maastrichtian aquifer of Senegal through multivariate statistical analysis and a GIS-based water quality index using physicochemical data from 232 boreholes distributed over the whole country. The aim was to 1) identify the water types and likely factors influencing the hydrochemistry, and 2) determine the suitability of groundwater for drinking and irrigation. Results showed that sodium, chloride, and fluoride are highly correlated with electrical conductivity (EC) reflecting the significant contribution of these elements to groundwater mineralization. The principal component analysis evidenced: 1) salinization processes (loaded by Na<sup>+</sup>, K<sup>+</sup>, EC, Cl<sup>-</sup>, F<sup>-</sup> and HCO<sub>3</sub>-</sup>) controlled by water/rock interaction, seawater intrusion and cation exchange reactions;2) dolomite dissolution loaded by the couple Ca<sup>2+</sup> and Mg<sup>2+</sup> and 3) localized mixing with upper aquifers and gypsum dissolution respectively loaded by NO<sub>3</sub>-</sup> and SO<sub>4</sub>2-</sup>. The hierarchical clustering analysis distinguished four clusters: 1) freshwater (EC = 594 μs/cm) with mixed-HCO<sub>3</sub> water type and ionic contents below WHO standard;2) brackish (Na-mixed) water type with moderate mineralization content (1310 μs/cm), 3) brackish (Na-Cl) water type depicted by high EC values (3292 μs/cm) and ionic contents above WHO and 4) saline water with Na-Cl water type and very high mineralization contents (5953 μs/cm). The mapping of the groundwater quality index indicated suitable zones for drinking accounting for 54% of the entire area. The occurrence of a central brackish band and its vicinity, which were characterized by high mineralization, yielded unsuitable groundwater for drinking and agricultural uses. The approach used in this study was valuable for assessing groundwater quality for drinking and irrigation, and it can be used for regional studies in other locations, particularly in shallow and vulnerable aquifers.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
为鉴定线香中主要挥发性成分,进一步揭示这些成分在不同样品间的差异及其对香气特征的贡献,采用顶空固相微萃取-气相色谱-质谱联用技术(headspace solid-phase microextraction gas chromatography-mass spectrometry,HS-SPME-GC-MS)结...为鉴定线香中主要挥发性成分,进一步揭示这些成分在不同样品间的差异及其对香气特征的贡献,采用顶空固相微萃取-气相色谱-质谱联用技术(headspace solid-phase microextraction gas chromatography-mass spectrometry,HS-SPME-GC-MS)结合多元统计分析方法对粘粉燃烧产物、金银花粉、金银花粉燃烧产物、线香燃烧产物中挥发性成分进行提取鉴定,并确定关键挥发性成分。结果表明,从4种样品中共鉴定出102种挥发性成分,以芳香族、杂环类、酮类化合物等成分为主。主成分分析、正交偏最小二乘法判别分析及聚类热图分析表明,金银花粉燃烧产物和线香燃烧产物挥发性成分组成相似,与粘粉燃烧产物及金银花粉挥发性成分存在较大差异,并筛选出25种投影变量的重要性(variable important for the projection,VIP)>1的关键挥发性成分。研究结果为金银花线香的进一步开发提供理论依据,也为中药材的应用拓展了新方向。展开更多
The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a...The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.展开更多
In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social developmen...In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.展开更多
With the implementation of General Senior High School Mathematics Curriculum Standards(2017 Edition,Revised in 2020),probability and statistics,as important carriers of the core mathematical competencies“mathematical...With the implementation of General Senior High School Mathematics Curriculum Standards(2017 Edition,Revised in 2020),probability and statistics,as important carriers of the core mathematical competencies“mathematical modeling”and“data analysis,”have increasingly highlighted their educational value.By summarizing the historical evolution of probability and statistics thinking and combining with teaching practice cases,this study explores its unique role in cultivating students’core mathematical competencies.The research proposes a project-based teaching strategy relying on real scenarios and empowered by technology.Through cases,it demonstrates how to use modern educational technology to realize the whole-process exploration of data collection,model construction,and conclusion verification,so as to promote the transformation of middle school probability and statistics teaching from knowledge imparting to competency development,and provide a practical reference for curriculum reform.展开更多
This paper focuses on the ideological and political construction of the course“Probability Theory and Mathematical Statistics.”Aiming at the current situation in teaching where emphasis is placed on knowledge impart...This paper focuses on the ideological and political construction of the course“Probability Theory and Mathematical Statistics.”Aiming at the current situation in teaching where emphasis is placed on knowledge imparting while value guidance is neglected,and combined with the requirements of ideological and political education policies in the new era,this paper explores the integration path between professional courses and ideological and political education.Through literature analysis,case comparison,and empirical research,the study proposes a systematic implementation plan covering the design of teaching objectives,the reconstruction of teaching content,and the optimization of the evaluation system.The purpose is to cultivate students’sense of social responsibility and innovative awareness by excavating the ideological and political elements in mathematics.The research results provide practical reference for colleges and universities to deepen the reform of ideological and political education in courses,and promote the implementation of the fundamental task of fostering virtue through education in STEM education.展开更多
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the National Natural Science Foundation of China(Grant No.U2142213)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grants 2025A1515011835,2022A1515011870)the National Natural Science Foundation of China(Grant No.42305167)。
文摘The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.
基金National Social Science Fund of China,No.24BTJ037Significant Project of the National Social Science Foundation of China,No.23&ZD102+1 种基金The Key Research Base for Philosophy and Social Sciences in Hangzhou:ESG and Sustainable Development Research Center,No.25JD053Zhejiang Provincial Statistical Scientific Research Project,No.25TJZZ12。
文摘Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.
基金Sponsored by the National Natural Science Foundation of China[grant number 12571305]the Natural Science Foundation of Shanghai[grant number 25ZR1401113].
文摘Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.
基金supported by (Faculty of Earth Science, University of Constantine 1)
文摘This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 from twelve hot springs and were analyzed for physicochemical parameters(electric conductivity, p H,total dissolved solids, temperature, Ca, Mg, Na, K, HCO_3,Cl, SO_4, and SiO_2). The waters of the thermal springs have temperatures varying from 28 to 51 °C and electric conductivity values ranging from 853 to 5630 l S/cm. Q-mode Cluster analysis resulted in the determination of two major water types: a Ca–HCO_3–SO_4 type with a moderate salinity and a Na–K–Cl type with high salinity. The plot of the major ions versus the saturation indices suggested that the hydrogeochemistry of thermal groundwater is mainly controlled by dissolution/precipitation of carbonate minerals, dissolution of evaporite minerals(halite and gypsum), and ion exchange of Ca(and/or Mg) by Na. The Gibbs diagram shows that evaporation is another factor playing a minor role. Principal Component Analysis produced three significant factors which have 88.2% of totalvariance that illustrate the main processes controlling the chemistry of groundwaters, which are respectively: the dissolution of evaporite minerals(halite and gypsum), ion exchange, and dissolution/precipitation of carbonate minerals. The subsurface reservoir temperatures were calculated using different cation and silica geothermometers and gave temperatures ranging between 17 and 279 °C. The Na–K and Na–K-Ca geothermometers provided high temperatures(up to 279 °C), whereas, estimated geotemperatures from K/Mg geothermometers were the lowest(17–53 °C). Silica geothermometers gave the most reasonable temperature estimate of the subsurface waters overlap between 20 and 58 °C, which indicate possible mixing with cooler Mg groundwaters indicated by the Na–K–Mg plot in the immature water field and in silica and chloride mixing models. The results of stable isotope analyses(δ^(18) O and δ~2 H) suggest that the origin of thermal water recharge is precipitation, which recharged from a higher altitude(600–1200 m) and infiltrated through deep faults and fractures in carbonate formations. They circulate at an estimated depth that does not exceed 2 km and are heated by a high conductive heat flow before rising to the surface through faults that acted as hydrothermal conduits.During their ascent to the surface, they are subjected to various physical and chemical changes such as cooling by conduction and change in their chemical constituents due to the mixing with cold groundwaters.
文摘This study evaluated the impact of chronicity (onset of injury to admission in-terval) on three domains of functional outcomes for a large group of traumatic brain injured (TBI) survivors. Subjects included 528 TBI adults who were treated in post-hospital residential rehabilitation centers. Subjects were assigned to one of three chronicity groups: 1) Early Interval (EI), 2.00 - 8.00 months n = 245, 2) Mid Interval (MI), 8.01 - 24.00 months n = 129, and (3) Late Interval (LI), 24.01 months and greater n = 154. Functional status was assessed with the MPAI-4. RM MANCOVA was applied to evaluate differences among groups from admission to discharge. Rasch analysis demonstrated satisfactory construct validity and internal consistency (Person reliability = 0.90 - 0.94, Item reliability = 0.99) for the admission and discharge MPAI-4s. Controlling for LOS and age, the RM MANCOVA revealed that each chronicity group showed significant improvement in MPAI-4 abilities, adjustment, and participation indices from admission to discharge (p < 0.001). Improvement observed from admission to discharge was the greatest among the EI group (p < 0.001). This study demonstrated the utility of multivariate statistical approaches for understanding the complexities of TBI treatment outcomes. As measured across three domains of functioning, rehabilitation was effective in reducing disability for participants in each chronicity group. Of the three groups, EI participants presented as the most disabled at admission but also made the greatest gains when assessed at discharge.
文摘This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and cardiovascular disease (CVD) emergency room (ER) visits and chronic obstructive pulmonary disease (COPD) ER visits using multivariate geostatistics in Houston, Texas, from 2004 to 2009. Analyses showed lack of strong pair-wise association among the predictors of O3, PM2.5, wind speed, relative humidity, and temperature. Whereas CVD and COPD ER visits exhibited a strong positive correlation. Both outcomes drastically increased from 2006 possibly due to immigration from neighboring locations. Parametric testing showed that the data differed significantly between the years. Multivariate multiple regression results on the 2009 data showed that PM2.5, relative humidity, and temperature were significant to both CVD and COPD ER visits. Codispersion coefficients were constant which justified the assumption of intrinsic correlation. That is, our predictors had strong influence on the spatial variability of CVD and COPD ER visits. This multivariate geostatistics approach predicted an increase of 34% in CVD ER visits and 24% increase in COPD ER visits, which calls for more attention from policy makers. The use of multivariate geostatistics analyses enabled us to successfully detect the effects of risk factors on both outcomes.
基金Supported by the National Marine Public Welfare Research Project(No.201305043)the National Natural Science Foundation of China(No.41106134)the National Key Technology R&D Program of China(Nos.2011BAD13B02,2010BAC68B01)
文摘Fatty acids(FAs) provide energy and also can be used to trace trophic relationships among organisms.Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months.We examined fatty acid profiles in aestivated and non-aestivated A.japonicus using multivariate analyses(PERMANOVA,MDS,ANOSIM,and SIMPER).The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly.The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers.Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation,and long chain FAs such as eicosapentaenoic(EPA) and docosahexaenoic acid(DHA) that produced from intestinal degradation,are digested during deep aestivation.Specific changes in the fatty acid composition of A.japonicus during aestivation needs more detailed study in the future.
文摘Surface sediment data acquired by the grab sampling technique were used in the present study to produce a high-resolution and full coverage surface grain-size mapping. The objective is to test whether the hypothetically natural relationship between the surface sediment distribution and complex bathymetry could be used to improve the quality of surface sediment patches mapping. This is based on our hypothesis that grain-size characteristics of the ridge surface sediments must be intrinsically related to the hydrodynamic condition, i.e. storm-induced currents and the geometry of the seabed morphology. The median grain-size data were obtained from grab samples with inclusive bathymetric point recorded at 713 locations on the high-energy and shallow shelf of the Spiekeroog Barrier Island at the German Bight of the Southern North Sea. The area features two-parallel shoreface-connected ridges which is situated obliquely WNW-SSE oriented and mostly sandy in texture. We made use the median grain-size (d50) as the predictand and the bathymetry as the covariable to produce a high-resolution raster map of median grain-size distribution using the Cokriging interpolation. From the cross-validation of the estimated median grain-size data with the measured ones, it is clear that the gradient of the linear regression line for Cokriging is leaning closer towards the theoretical perfect-correlation line (45°) compared to that for Anisotropy Kriging. The interpolation result with Cokriging shows more realistic estimates on the unknown points of the median grain-size and gave detail to surface sediment patchiness, which spatial scale is more or less in agreement with previous studies. In addition to the moderate correlation obtained from the Pearson correlation (r = 0.44), the cross-variogram shows a more precise nature of their spatial correlation, which is physically meaningful for the interpolation process. The present study partially contributes to the framework of habitat mapping and nature protection that is to fill the gaps in physical information in a high-energetic and shallow coastal shelf.
文摘Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.
文摘A regional groundwater quality evaluation was conducted in the deep Maastrichtian aquifer of Senegal through multivariate statistical analysis and a GIS-based water quality index using physicochemical data from 232 boreholes distributed over the whole country. The aim was to 1) identify the water types and likely factors influencing the hydrochemistry, and 2) determine the suitability of groundwater for drinking and irrigation. Results showed that sodium, chloride, and fluoride are highly correlated with electrical conductivity (EC) reflecting the significant contribution of these elements to groundwater mineralization. The principal component analysis evidenced: 1) salinization processes (loaded by Na<sup>+</sup>, K<sup>+</sup>, EC, Cl<sup>-</sup>, F<sup>-</sup> and HCO<sub>3</sub>-</sup>) controlled by water/rock interaction, seawater intrusion and cation exchange reactions;2) dolomite dissolution loaded by the couple Ca<sup>2+</sup> and Mg<sup>2+</sup> and 3) localized mixing with upper aquifers and gypsum dissolution respectively loaded by NO<sub>3</sub>-</sup> and SO<sub>4</sub>2-</sup>. The hierarchical clustering analysis distinguished four clusters: 1) freshwater (EC = 594 μs/cm) with mixed-HCO<sub>3</sub> water type and ionic contents below WHO standard;2) brackish (Na-mixed) water type with moderate mineralization content (1310 μs/cm), 3) brackish (Na-Cl) water type depicted by high EC values (3292 μs/cm) and ionic contents above WHO and 4) saline water with Na-Cl water type and very high mineralization contents (5953 μs/cm). The mapping of the groundwater quality index indicated suitable zones for drinking accounting for 54% of the entire area. The occurrence of a central brackish band and its vicinity, which were characterized by high mineralization, yielded unsuitable groundwater for drinking and agricultural uses. The approach used in this study was valuable for assessing groundwater quality for drinking and irrigation, and it can be used for regional studies in other locations, particularly in shallow and vulnerable aquifers.
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.
文摘为鉴定线香中主要挥发性成分,进一步揭示这些成分在不同样品间的差异及其对香气特征的贡献,采用顶空固相微萃取-气相色谱-质谱联用技术(headspace solid-phase microextraction gas chromatography-mass spectrometry,HS-SPME-GC-MS)结合多元统计分析方法对粘粉燃烧产物、金银花粉、金银花粉燃烧产物、线香燃烧产物中挥发性成分进行提取鉴定,并确定关键挥发性成分。结果表明,从4种样品中共鉴定出102种挥发性成分,以芳香族、杂环类、酮类化合物等成分为主。主成分分析、正交偏最小二乘法判别分析及聚类热图分析表明,金银花粉燃烧产物和线香燃烧产物挥发性成分组成相似,与粘粉燃烧产物及金银花粉挥发性成分存在较大差异,并筛选出25种投影变量的重要性(variable important for the projection,VIP)>1的关键挥发性成分。研究结果为金银花线香的进一步开发提供理论依据,也为中药材的应用拓展了新方向。
基金supported by the funding from the Humanities and Social Science Fund of Ministry of Education of China(No.22YJCZH028)National Natural Science Foundation of China(Grant No.72303001)+3 种基金Fundamental Research Funds for the Central Universities(No.JUSRP124043)Anhui Provincial Excellent Young Scientists Fund for Universities(No.2024AH030001)Anhui Education Department Excellent Young Teachers Fund(No.YQYB2024021)Basic Research Program of Jiangsu(No.BK20251593)。
文摘The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.
文摘In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.
基金2021 Annual Research Project of Yili Normal University(2021YSBS012)。
文摘With the implementation of General Senior High School Mathematics Curriculum Standards(2017 Edition,Revised in 2020),probability and statistics,as important carriers of the core mathematical competencies“mathematical modeling”and“data analysis,”have increasingly highlighted their educational value.By summarizing the historical evolution of probability and statistics thinking and combining with teaching practice cases,this study explores its unique role in cultivating students’core mathematical competencies.The research proposes a project-based teaching strategy relying on real scenarios and empowered by technology.Through cases,it demonstrates how to use modern educational technology to realize the whole-process exploration of data collection,model construction,and conclusion verification,so as to promote the transformation of middle school probability and statistics teaching from knowledge imparting to competency development,and provide a practical reference for curriculum reform.
基金Shaanxi Provincial 14th Five-Year Plan for Educational Science Research(SGH24Q481)。
文摘This paper focuses on the ideological and political construction of the course“Probability Theory and Mathematical Statistics.”Aiming at the current situation in teaching where emphasis is placed on knowledge imparting while value guidance is neglected,and combined with the requirements of ideological and political education policies in the new era,this paper explores the integration path between professional courses and ideological and political education.Through literature analysis,case comparison,and empirical research,the study proposes a systematic implementation plan covering the design of teaching objectives,the reconstruction of teaching content,and the optimization of the evaluation system.The purpose is to cultivate students’sense of social responsibility and innovative awareness by excavating the ideological and political elements in mathematics.The research results provide practical reference for colleges and universities to deepen the reform of ideological and political education in courses,and promote the implementation of the fundamental task of fostering virtue through education in STEM education.
