Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

基于DCC-GARCH模型的绿色股票市场流动性风险溢出效应研究

随着全球绿色金融领域的不断发展,绿色股票市场与传统金融市场的联动性呈现出增强趋势,绿色股票市场的流动性风险对传统金融市场产生显著影响。本文选取2021年7月29日至2023年4月26日之间的国政香蜜湖绿色金融指数、恒生指数和深证成指,分别构建DCC-GARCH模型分析绿色股票市场与传统金融市场间的流动性风险关系。结果表明:当前我国绿色股票市场存在流动性水平较低且波动较大的特征;随着时间推移,香港股票市场对绿色股票市场的风险溢出逐渐趋于稳定,但深圳股票市场的风险溢出持续呈现出较大波动。在此基础上,本文提出应建立风险预警机制并引导资金流入市场,将流动性风险的预测和溢出效应分析纳入日常风险管理中,同时完善绿色企业奖惩机制以及加强绿色投资宣传和推广力度。

Utilization of biomarkers for the prognostic prediction of cardiac arrest survivors using a multi-modal approach

International guidelines for post-cardiac arrest care recommend using multi-modal strategies to avoid the withdrawal of life-sustaining therapy(WLST)in patients with the potential for neurological recovery.[1]However,a clear methodology for multi-modal approaches has yet to be developed.Neuron-specific enolase(NSE)is currently the only recommended biomarker,and the European Resuscitation Council(ERC)and the European SocietyofIntensiveCareMedicine(ESICM)have proposed a cutoff value of 60μg/L at 48 and/or 72 h after the return of spontaneous circulation(ROSC)as a multimodal prognostic tool for predicting poor neurological outcomes.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

The role of multimodality imaging in calcified valves with infective endocarditis

A 68-years old woman presented with ahistory of recurrent fever of 38-39°ac-companied by chills and weakness over the past month.Her physical examination was unremarkable except for an audible 3/6 ejection murmur at the 2nd right intercostal space.Her vital signs were normal with no fever at presentation.Laboratory tests showed elevated white blood count of 11,800cells/mm3 with a remarkable neutrophilia and elevated C-reactive protein of 14 mg/dL.Blood glucose,renal and liver function tests were all normal.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

基于Prophet-DCC-GARCH组合模型的国债与股票投资组合VaR估计

随着经济社会的发展,经济周期波动、市场不确定性加剧,风险值(VaR)的预测迫在眉睫。文章采用风险型的科技股票比亚迪和稳健型的国债的收盘数据,以DCC-GARCH模型为基础,引入Prophet模型,更改DCC-GARCH模型的均值项,考虑节假日、特殊事件和年周期性、周周期性,构建了Prophet-DCC-GARCH组合模型。该组合模型的预测误差(RMSE)为:比亚迪3.044 959×10^(-4),国债7.760 821×10^(-6)。采用计量经济学方法计算VaR,在置信度为0.95%时,2个头寸的联合风险值为:DCC-GARCH模型0.038 8,Prophet-DCC-GARCH组合模型0.037 0。组合模型中,Prophet模型提取了潜在风险影响因素,所以风险值明显要小。在对多个头寸的风险值进行预测时,考虑其中潜在的影响因素,可以更好地提供VaR预测值,进而为投资者提供更可靠的建议。

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

基于DCC-GARCH的海上风电场出力空间相关性分析及预测

多座海上风电场出力之间存在一定的空间相关性,构建合适的风电出力相关性模型有助于提高风电出力的预测精度。针对空间相关性具有时变特性以及难以描述和衡量,提出基于动态条件相关广义自回归条件异方差(DCC-GARCH)模型的海上风电场出力相关性模型。利用多维正态分布和DCC-GARCH模型拟合多风电场的皮尔森相关系数,求解随时间变化的风电场出力空间相关系数,在准确表征空间相关性大小的同时体现空间相关性的时序变化特征。基于DCC-GARCH模型建立多座风电场出力动态空间相关性短期预测模型。基于江苏省盐城市海上风电场数据进行算例分析,结果验证了所提方法的合理性和有效性。

房地产业与商业银行间风险溢出效应研究--基于DCC-GARCH-CoVaR模型

2020年新冠肺炎疫情的爆发成为我国市场经济发展的巨大阻碍,疫情冲击下,房地产业与银行业作为我国重要行业也受到极大影响,基于此背景,对我国房地产业与三类上市商业银行(国有银行、城市银行、股份制银行)股市风险溢出进行研究,首先构建各收益率序列ARMA-GARCH模型,接着对房地产业与三类商业银行分别拟合DCC-GARCH模型,以此研究两行业的动态相关关系,最后通过该模型计算CoVaR值、ΔCoVaR、%ΔCoVaR值来度量两行业间风险溢出效应强度.实证研究结果表明,疫情爆发前期,房地产业与商业银行间基本为正向动态相关关系,且两者间存在正向风险溢出效应,随着疫情的爆发,两行业间的正向动态相关性显著增强且风险溢出效应也随之增强,均为正向溢出,其中对股份制商业银行影响较大.

基于EMD-DCC-GARCH的沪深300股指期货多尺度动态套期保值研究

大多已有研究均忽视不同时间期限对套期保值的影响,本文利用经验模态分解方法(Empirical Mode Decomposition,EMD)将沪深300指数现货和期货收益率分为短期、中期和长期三个时间尺度。进一步结合DCC-GARCH模型,分别在最小方差和最小CVaR的套期保值框架下研究沪深300指数期货的多时间尺度动态套期保值问题,估计最优套期保值比率,并将动态DCC-GARCH模型的套期保值绩效与传统静态模型的绩效进行对比。实证结果表明,随着时间尺度的增加,最优套期保值比率逐渐降低。DCC-GARCH模型在原始尺度和短期尺度表现优于静态套期保值模型,但不适用于中长期尺度的估计。DCC-GARCH模型中,利用最小CVaR法计算的套期保值绩效优于利用最小方差法计算的结果。

中国金融市场风险溢出效应及其时空特征——基于溢出指数方法与DCC-GARCH模型

运用溢出指数模型和DCC-GARCH模型对我国2005年7月22日至2021年8月27日七大金融市场的风险溢出效应及其时空特征做出了全面分析。结果显示:在时间维度上,中国金融市场风险溢出指数在18%~52%之间波动,动态相关系数在0.09至0.31间变动,当重要政策和事件冲击时金融市场风险溢出程度明显增强,并且风险溢出水平随时间的累积和消减具有非对称性特征。在空间维度上,金融市场间存在非对称溢出效应。房地产、商品和股票市场的风险净溢出指数为正,黄金、货币、外汇和债券市场的风险净溢出指数为负,商品和黄金市场、股票和房地产市场以及债券与黄金市场间的风险关联性较大。

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

中国黄金与行业股票市场的动态相关性研究——基于DCC-GARCH模型

利用2015—2023年的黄金AU9999与十个上证行业指数日收益率数据,建立DCC-GARCH模型来分析动态相关性并计算最优投资权重和最优对冲比率。实证结果发现,黄金与行业股票市场存在动态相关性;黄金市场对十个行业的避险和对冲作用有差异,市场极端情况下黄金与行业指数均高度负相关,熊市期间黄金与原材料以外的九个行业具有不同程度负相关,黄金与工业、可选消费等七个行业长期具有负相关性;黄金平均权重在主要消费行业占比最高,原材料行业的对冲比率最高。最后提出资产组合纳入黄金、持续评估和区分行业进行风险管理的建议。

Development of a software platform for bridge modal and damage identification based on ambient excitation

Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace identification,peak-picking,and frequency domain decomposition method in modal analysis based on ambient excitation,and the effectiveness of these three methods is verified through finite element calculation and numerical simulation,Then the damage element is added to the finite element model to simulate the crack,and the curvature mode difference and the curvature mode area difference square ratio are calculated by using the stochastic subspace identification results to verify their ability of damage identification and location.Finally,the above modal and damage identification techniques are integrated to develop a bridge modal and damage identification software platform.The final results show that all three modal identification methods can accurately identify the vibration frequency and mode shape,both damage identification methods can accurately identify and locate the damage,and the developed software platform is simple and efficient.

“一带一路”沿线国家与我国金融市场互联效应研究——基于多元DCC-GARCH模型的分析

本文以2011年1月1日至2020年12月31日间中国上证指数与六个“一带一路”沿线国家股票市场周收益率为数据样本,通过建立多元DCC-GARCH模型获取各市场与中国市场的动态相关系数矩阵和走势图,并分析了关联变化特征。结果显示,“一带一路”倡议提出以来,各国与中国的金融互联效应都有所增强,其中以新加坡为代表的东南亚市场与中国的互联效应增加最为显著,动态相关系数的脉冲高度和频率都明显增强,说明其互联效应更倾向于受共同事件影响而驱动。而俄罗斯与中国的互联效应增进呈现缓和的线性趋势,显示其与中国的互联效应更多的是由于实体往来的联系增加。另外,巴基斯坦和匈牙利与中国的互联性虽有所增强,但并不显著,可以进一步增强与相关地区的金融互联互通。

The effect of Typhoon Kalmaegi on the modal energy and period of internal waves near the Dongsha Islands(South China Sea)

The influence of Typhoon Kalmaegi on internal waves near the Dongsha Islands in the northeastern South China Sea was investigated using mooring observation data.We observed,for the first time,that the phenomenon of regular variation characteristics of the 14-d spring-neap cycle of diurnal internal tides(ITs)can be regulated by typhoons.The diurnal ITs lost the regular variation characteristics of the 14-d spring-neap cycle during the typhoon period owing to the weakening of diurnal coherent ITs,represented by O_(1)and K_(1),and the strengthening of diurnal incoherent ITs.Results of quantitative analysis showed that during the pre-typhoon period,timeaveraged modal kinetic energy(sum of Modes 1–5)of near-inertial internal waves(NIWs)and diurnal and semidiurnal ITs were 0.62 kJ/m^(2),5.66 kJ/m^(2),and 1.48 kJ/m^(2),respectively.However,during the typhoon period,the modal kinetic energy of NIWs increased 5.11 times,mainly due to the increase in high-mode kinetic energy.At the same time,the modal kinetic energy of diurnal and semidiurnal ITs was reduced by 68.9%and 20%,respectively,mainly due to the decrease in low-mode kinetic energy.The significantly reduced diurnal ITs during the typhoon period could be due to:(1)strong nonlinear interaction between diurnal ITs and NIWs,and(2)a higher proportion of high-mode diurnal ITs during the typhoon period,leading to more energy dissipation.