Sensitivity Analysis of Structural Dynamic Behavior Based on the Sparse Polynomial Chaos Expansion and Material Point Method

This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.

Probabilistic analysis of tunnel face seismic stability in layered rock masses using Polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.

Experimental test of an extension of the Rosenzweig-Porter model to mixed integrable-chaotic systems experiencing time-reversal invariance violation

We report on the theoretical and experimental investigations of the transition of a typical quantum system with mixed regular-integrable classical dynamics to one with violated time-reversal(T)invariance.The measurements are performed with a flat superconducting microwave resonator with circular shape in which chaoticity is induced by using either long antennas or inserting two circular disks into the cavity,and by magnetizing a ferrite disk placed at its center,which leads to violation of T invariance.We propose an extension of the Rosenzweig-Porter(RP)model,which interpolates between mixed regular-chaotic instead of integrable dynamics and fully chaotic dynamics with violated T-invariance,and derive a Wigner-surmise like analytical expression for the corresponding nearest-neighbor spacing distribution.We propose a procedure involving this result and those for the RP model to determine the size of T-invariance violation and chaoticity and validate it employing the experimental eigenfrequency spectra.

A potential giant gallium deposit hosted in the tailing dam of the Fankou Zn-Pb deposit in northern Guangdong Province, South China

1.Objective The Fankou giant zinc(Zn)-lead(Pb)deposit in Guangdong Province is well-known for its huge reserves of over 10 Mt(million ton)Zn+Pb metals and high ore-grade with Zn+Pb exceeding 15%(Guangdong Fankou Deposit Investigation Group,1980;Zhu XY et al.,2017).After 60 years of exploration and exploitation,the deposit has accumulated millions of tons of tailings.One interesting question is that what components are hosted in these tailings,and whether some key and critical metals such as gallium and germanium are extremely enriched and worth further comprehensive utilization.This motivated us to conduct an integrated set of field investigation,sampling,major-trace element analyses and reserve assessments about the Fankou tailings.In this contribution,we discover that the tailings contain a variety of metal elements such as zinc,lead and gallium with comprehensive utilization potential.These data can act as a scientific guidance for further exploration and utilization of the Fankou tailings,and aid to enhance the utilization efficiency of the key mineral resource in China.Our study also provide demonstration for future green mine construction in China.

Suppression and synchronization of chaos in uncertain time-delay physical system

The mechanical horizontal platform(MHP)system exhibits a rich chaotic behavior.The chaotic MHP system has applications in the earthquake and offshore industries.This article proposes a robust adaptive continuous control(RACC)algorithm.It investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent disturbances.The closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the closed-loop.The proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control inputs.This paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability technique.It provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant values.The computer simulation results endorse the theoretical findings and provide a comparative performance.

A novel variable-order fractional chaotic map and its dynamics

In recent years,fractional-order chaotic maps have been paid more attention in publications because of the memory effect.This paper presents a novel variable-order fractional sine map(VFSM)based on the discrete fractional calculus.Specially,the order is defined as an iterative function that incorporates the current state of the system.By analyzing phase diagrams,time sequences,bifurcations,Lyapunov exponents and fuzzy entropy complexity,the dynamics of the proposed map are investigated comparing with the constant-order fractional sine map.The results reveal that the variable order has a good effect on improving the chaotic performance,and it enlarges the range of available parameter values as well as reduces non-chaotic windows.Multiple coexisting attractors also enrich the dynamics of VFSM and prove its sensitivity to initial values.Moreover,the sequence generated by the proposed map passes the statistical test for pseudorandom number and shows strong robustness to parameter estimation,which proves the potential applications in the field of information security.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

The Guangdong-Hong Kong-Macao Greater Bay Area Business Gathering Amplifies Global Charm

China’s Guangdong-Hong Kong-Macao Greater Bay Area(GBA)is becoming a world-class business hub where global firms can thrive,members of the global business community said at a conference held in Hong Kong recently.

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

Can spatial planning in the Polish Carpathians be enforced? How spatial chaos indicators depict a gap between scientific knowledge and practice

Despite their strategic hydrological importance for neighbouring areas,the Polish Carpathians are experiencing spatial chaos,which may weaken their adaptability to the progressive climate change.The article attempts to answer the question of whether spatial planning,which is supposed to guarantee spatial order,fulfils its role and whether the knowledge of the natural conditions of spatial development is respected in the spatial planning process.Using GIS techniques,up to 238 communes were analysed in terms of their spatial coverage,the degree of scattered settlement,and the violation of natural barriers by location of buildings in areas that are threatened with mass movements or floods;by settlement on excessively inclined slopes and in areas with adverse climatic conditions.Spearman non-parametric rank correlation analysis and the multidimensional Principal Component Analysis(PCA)technique were performed to investigate relations between spatial chaos indicators and the planning situation.The analysis of the data has revealed that spatial planning does not fulfil its role.Serious errors in location of buildings have been noted even though the communes are covered by local spatial development plans.Scientific knowledge is not sufficiently transferred into planning documents,and bottom-up initiatives cannot replace systemic solutions.There is a need for strengthening the role of environmental studies documents in the spatial planning system.This would facilitate the transfer of scientific knowledge into the planning process and help to protect mountain areas.The development of a special spatial strategy for the Polish Carpathians in compliance with the Carpathian Convention is also recommended.

Scale effect of coastal landscape pattern stability and driving forces:a case study of Guangdong Province,China

The long-term dynamic evolution and underlying mechanisms of coastal landscape pattern stability,driven by strong anthropogenic interference and consequently climate change,are topics of major interest in national and international scientific research.Guangdong Province,located in southeastern China,has been undergoing rapid urbanization over several decades.In this study,we quantitatively determined the scale threshold characteristics of coastal landscape pattern stability in Guangdong Province,from the dual perspective of spatial heterogeneity and spatial autocorrelation.An analysis of the spatiotemporal evolution of the coastal landscape was conducted after the optical scale was determined.Then,we applied the geodetector statistical method to quantitatively explore the mechanisms underlying coastal landscape pattern stability.Based on the inflection point of landscape metrics and the maximum value of the MoranⅠindex,the optimal scale for analyzing coastal landscape pattern stability in Guangdong Province was 240 m×240 m.Within the past several decades,coastal landscape pattern stability increased slightly and then decreased,with a turning point around 2005.The most significant variations in coastal landscape pattern stability were observed in the transition zone of rural-urban expansion.A q-statistics analysis showed that the explanatory power of paired factors was greater than that of a single driving factor;the paired factors with the greatest impact on coastal landscape pattern stability in Guangdong Province were the change in gross industrial output and change in average annual precipitation from 2010 to 2015,based on a q value of 0.604.These results will contribute to future efforts to achieve sustainable coastal development and provide a scientific basis and technical support for the rational planning and utilization of resources in large estuarine areas,including marine disaster prevention and seawall ecological restoration.

基于小样本下改进ChaosNet的轴承故障诊断

为解决在训练样本不足条件下,轴承故障特征提取困难的问题,提出一种基于改进神经混沌学习(neurochaos learning+AdaBoost,NL-AdaBoost)的轴承故障诊断新方法。首先,对时域振动信号进行快速傅里叶变换(fast fourier transform,FFT)提取频域特征,拼接时频域信号获得一维特征样本;其次,输入信号产生对混沌GLS神经元的激励,形成ChaoFEX特征,馈送至集成学习分类器(AdaBoost);随后,选取轴承故障特征样本,对样本集做k折交叉验证,获得模型最优超参数值,将其应用于测试集进行模型分类能力验证;最后,在小样本对比实验中,与4种常见深度学习算法比较模型的macro F1-score。实验结果证明,在低训练样本条件下,NL-AdaBoost模型具有良好的准确性和泛化能力。

Mechanism of Guangdong Shenqu in regulating intestinal flora in mice with food stagnation and internal heat based on 16S rDNA sequencing

Objective:To investigate the effect of Guangdong Shenqu(GSQ)on intestinal flora structure in mice with food stagnation through 16S rDNA sequencing.Methods: Mice were randomly assigned to control,model,GSQ low-dose(GSQL),GSQ medium-dose(GSQM),GSQ high-dose(GSQH),and lacidophilin tablets(LAB)groups,with each group containing 10 mice.A food stagnation and internal heat mouse model was established through intragastric administration of a mixture of beeswax and olive oil(1:15).The control group was administered normal saline,and the model group was administered beeswax and olive oil to maintain a state.The GSQL(2 g/kg),GSQM(4 g/kg),GSQH(8 g/kg),and LAB groups(0.625 g/kg)were administered corresponding drugs for 5 d.After administration,16S rDNA sequencing was performed to assess gut microbiota in mouse fecal samples.Results: The model group exhibited significant intestinal flora changes.Following GSQ administration,the abundance and diversity index of the intestinal flora increased significantly,the number of bacterial species was regulated,andαandβdiversity were improved.GSQ administration increased the abundance of probiotics,including Clostridia,Lachnospirales,and Lactobacillus,whereas the abundance of conditional pathogenic bacteria,such as Allobaculum,Erysipelotrichaceae,and Bacteroides decreased.Functional prediction analysis indicated that the pathogenesis of food stagnation and GSQ intervention were primarily associated with carbohydrate,lipid,and amino acid metabolism,among other metabolic pathways.Conclusion: The digestive mechanism of GSQ may be attributed to its role in restoring diversity and abundance within the intestinal flora,thereby improving the composition and structure of the intestinal flora in mice and subsequently influencing the regulation of metabolic pathways.

Sensitivity Analysis of Electromagnetic Scattering from Dielectric Targets with Polynomial Chaos Expansion and Method of Moments

In this paper,an adaptive polynomial chaos expansion method(PCE)based on the method of moments(MoM)is proposed to construct surrogate models for electromagnetic scattering and further sensitivity analysis.The MoM is applied to accurately solve the electric field integral equation(EFIE)of electromagnetic scattering from homogeneous dielectric targets.Within the bistatic radar cross section(RCS)as the research object,the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate model.The corresponding sensitivity results are given by the further derivative operation,which is compared with those of the finite difference method(FDM).Several examples are provided to demonstrate the effectiveness of the proposed algorithm for sensitivity analysis of electromagnetic scattering from homogeneous dielectric targets.

Three-dimensional pseudo-dynamic reliability analysis of seismic shield tunnel faces combined with sparse polynomial chaos expansion

To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.

Zhongshan:Rising Dragon of Guangdong

My journey to Zhongshan City in Guangdong Province alongside experts,foreign correspondents,and local Chinese journalists proved to be an enlightening exploration of the intricacies of China’s development,especially within its smaller cities.While a brief five-day sojourn wasn’t sufficient for intimacy with the city’s entirety,the immersion into Zhongshan’s cultural,historical,and modern facets,including Zhongshan Museum,urban planning zones,business hubs,an industrial park,ancient villages,Dr.Sun Yat-sen Museum,and a monumental bridge project,left me with the impression that the city is awakening like a dragon ascending from the heart of Guangdong.

Generalized polynomial chaos expansion by reanalysis using static condensation based on substructuring

This paper presents a new computational method for forward uncertainty quantification(UQ)analyses on large-scale structural systems in the presence of arbitrary and dependent random inputs.The method consists of a generalized polynomial chaos expansion(GPCE)for statistical moment and reliability analyses associated with the stochastic output and a static reanalysis method to generate the input-output data set.In the reanalysis,we employ substructuring for a structure to isolate its local regions that vary due to random inputs.This allows for avoiding repeated computations of invariant substructures while generating the input-output data set.Combining substructuring with static condensation further improves the computational efficiency of the reanalysis without losing accuracy.Consequently,the GPCE with the static reanalysis method can achieve significant computational saving,thus mitigating the curse of dimensionality to some degree for UQ under high-dimensional inputs.The numerical results obtained from a simple structure indicate that the proposed method for UQ produces accurate solutions more efficiently than the GPCE using full finite element analyses(FEAs).We also demonstrate the efficiency and scalability of the proposed method by executing UQ for a large-scale wing-box structure under ten-dimensional(all-dependent)random inputs.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Evaluation of Comprehensive Carrying Capacity of Tourist Cities and Optimization Paths:Based on the Comparison between East and West Guangdong

Seven tourist cities in eastern and western Guangdong were selected as the research objects to establish an evaluation index system of urban comprehensive carrying capacity,and its changing laws were analyzed.It was found that the comprehensive carrying capacities of cities in eastern and western Guangdong showed a trend of“first increasing and then decreasing”from 2015 to 2021,and reached the highest point in 2019,but there were significant differences among regions.From the perspective of spatial distribution,the comprehensive carrying capacities of cities in eastern and western Guangdong generally presented the law of high on both sides and low in the middle.In terms of the proportion of comprehensive carrying capacity of tourist cities,the larger part was always the carrying capacity of infrastructure and public services.The value of economic carrying capacity showed a trend of“first increasing and then decreasing”,while the value of environmental carrying capacity was always on the increase,and the value of tourism resources carrying capacity was basically stable.Finally,according to the analysis results,this paper put forward the optimization paths for comprehensive carrying capacities of tourist cities in eastern and western Guangdong from following four aspects:coordinating regional development,rationally utilizing natural resources,adjusting economic structure and adhering to the sustainable development concept.

Research on the Digital Transformation of the Whole Industry Chain of Guangdong Supply and Marketing Cooperative

Guangdong Supply and Marketing Cooperative serves as an important institution for the“Three Rural Areas”in the Guangdong-Hong Kong-Macao Greater Bay Area.Its digital transformation and optimization serve as powerful supports for rural revitalization.This paper systematically reviews the main practices and effectiveness of the digital transformation within the Guangdong Supply and Marketing Cooperative.It also analyzes the current development dilemmas faced in the construction of digital supply and marketing.Additionally,it proposes targeted solutions,including building a big data resource base,optimizing the digital supply and marketing cloud platform,developing digital publictype agricultural social service applications,establishing a new model of rural e-commerce,enhancing the traceability management system for agricultural products,and strengthening the construction of the digital human resources system.These proposals aim to further promote the strategy for revitalizing the countryside.