Framework Of Dynamic Model Research Articles

Time-Variation Damping Dynamic Modeling and Updating for Cantilever Beams with Double Clearance Based on Experimental Identification

The accuracy of a space manipulator’s end trajectory and stability is significantly affected by joint clearance. Aiming to improve the prediction accuracy of vibration caused by clearance, a dynamic clearance modeling method is developed based on parameter identification in this study. First, a dynamic model framework for manipulator arms is established based on the Hamilton principle and hypothetical mode method with time-variation damping. Then, a multi-resolution identification is performed for identifying the instantaneous frequency and damping ratio to estimate stiffness and damping by the sensors. The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. Finally, a case study is conducted to verify the presented model. In comparison with the initial dynamic model based on constant damping, the modal assurance criterion (MAC) of the proposed improved model based on time-variation damping is improved by 43.97%, the mean relative error (MRE) of the frequency response function (FRF) is reduced by 32.6%, and the root mean square error (RMSE) is reduced by 18.19%. The comparison results indicate the advantages of the proposed model. This modeling method could be used for vibration prediction in control systems for space manipulators to improve control accuracy.

Exploring the impact and mechanisms of digital finance on regional financial development in China

ABSTRACT This study investigates the effects and mechanisms of digital finance on regional financial development in China. Using panel data from 31 provinces spanning 2009 to 2022, we employ the System Generalized Method of Moments (GMM) estimator within a dynamic panel model framework. Our findings indicate a significant positive impact of digital finance on regional financial development, particularly in provinces with higher levels of digital finance adoption. Further analysis reveals that digital finance promotes regional financial development by facilitating digital technology implementation and fostering regional innovation.

Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling.

Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models. The proposed method achieves robust circuit inference by iteratively integrating parameter estimates from local microscale and global meso- or macroscale circuits, derived from signals across various scales and modalities. Parameters estimated from high-resolution data within specific regions inform global circuit estimation by constraining neural properties in unobserved regions, while large-scale circuit data help elucidate detailed local circuitry. Using a virtual ground truth system, we validated the method across diverse experimental settings, combining calcium imaging (CaI), voltage-sensitive dye imaging (VSDI), and blood-oxygen-level-dependent (BOLD) signals-each with distinct coverage and resolution. Our reciprocal and iterative parameter estimation approach markedly improves the accuracy of neural property and connectivity estimates compared to traditional one-step estimation methods. This iterative integration of local and global parameters presents a reliable approach to inferring extensive, complex neural circuits from partially observed, multimodal, and multiscale data, showcasing how information from different scales reciprocally enhances entire circuit parameter estimation.

Distribution and Long-Term Variation of Wetland Land Cover Types in the Yellow River Delta Remote Sensing Monitoring

Wetlands are dubbed the “kidneys of the earth” and are involved in climate regulation, carbon sequestration, ecological balance preservation, and reducing the surface water pollution. Ongoing economic development has introduced pressing challenges to wetland environments. In this context, extracting coastal wetland information and monitoring the dynamic changes are essential. Using long-term sequence Sentinel-2 satellite remote sensing images and field observations, this research proposed a Dynamic Bayesian Network classification model framework based on conjugate gradient updates. We compared the wetland feature extraction effects of the Fletcher–Reeves and the Polak–Ribière–Polyak algorithms of the conjugate gradient. Then, remote sensing combined with the FRDBN classification model was used to extract the information pertinent to wetland feature types and changes in wetland areas and analyze alterations in the distribution characteristics of land cover types. The results showed that the FRDBN model achieved high accuracy (above 96%), and kappa coefficients exceeded 0.96. Long-term monitoring revealed that the area of wetlands increased by 0.85 × 104 hm2 from 2016 to 2021. Non-aquatic land cover types exhibited pronounced dynamic changes, with the area of change representing 58–69% of the monitored total. Specifically, the transition between salt marsh vegetation and artificial wetlands was relatively obvious. The FRDBN model provides a new method for extracting wetland feature information. Wetland protection, dynamic monitoring, and carbon sink research can provide robust technology support, facilitating investigations into coastal salt marsh carbon sinks and technological advances in carbon sink assessment.

An Analytical Framework for Global Dynamic Modeling of Flexible Variable Topology Mechanisms

The coupling of topology transition with flexible deformation and rigid motion presents significant challenges in the dynamic modeling of flexible variable topology mechanisms. Existing dynamics models are mostly special-purpose models for their particular cases and thus struggle to completely depict the general topology transition characteristics. To address this gap, this paper proposes an analytical framework for the global dynamic modeling of flexible variable topology mechanisms, focusing on general cases. Initially, the flexible variable topology mechanisms are rigorously defined by the ordered triples and the general topology transition approaches are presented. A novel concept, the “basic flexible kinematic chain set”, is suggested, which can easily transform into the topology of each submechanism by slightly extending. Based on this concept, basic and conditional constraints are established. The continuous dynamic modeling method for each topology is developed using Jourdain’s principle and the Lagrange multiplier method. Additionally, three classes of constraints related to topology transition are identified, and their equations are formulated, elucidating the topology transition nature. Compatibility equations are proposed to define the new coordinate system for describing the deformation of flexible components after the topology transition. An impact dynamic equation is established to describe abrupt velocity change. Integrating compatibility and impact equations, a discontinuous dynamic modeling method for topology transition is developed. Finally, a flexible variable topology mechanism example is studied, and simulations and experiments are conducted to validate the proposed framework. This analytical framework is general-purpose and efficient, guiding the global dynamic modeling of various flexible variable topology mechanisms and the development of sophisticated control techniques.

Influence of information flow topology and maximum platoon size on mixed traffic stability

Beyond platooning with pure connected and automated vehicles (CAVs), mixed platooning with both CAVs and human-driven vehicles (HDVs) emerges as a practical formation pattern in mixed traffic flow. This paper investigates how the two types of information flow topology (IFT) as “looking ahead” or “looking behind”, alongside the maximum size of mixed platoons, influence the stability of the entire traffic flow. Precisely, the mixed traffic system is partitioned into three subsystems: independent HDVs, independent CAVs, and mixed platoons, and a dynamical modeling framework is established under two types of specific IFT: Multi-Predecessor Following (MPF) for “looking ahead” and Multi-Successor Leading (MSL) for “looking behind”. Then, a unified string stability evaluation method is proposed, which captures the role of IFT and maximum platoon size, the latter being associated with the emergence probability of different subsystems and CAV penetration rates. Practical considerations, such as the heterogeneity of vehicle dynamics and the absence of HDV communication capabilities, are also incorporated. Extensive numerical analysis and nonlinear traffic simulations reveal that “looking behind” shows superior performance in mitigating traffic perturbations, especially at low penetration rates, compared with the prevailing choice of “looking ahead”. In addition, increasing the platoon size could further enhance traffic flow stability. These results provide practical insights into the design of IFT and platoon size for CAV cooperation in future mixed traffic environments.

Clustering-based agent system (CAS) to simulate the energy-related behaviours of office occupants

Rapid urbanization and building sector growth emphasize the critical role of energy conservation in addressing global energy consumption and greenhouse emissions. Despite advancements in energy-efficient technologies, an ‘energy performance gap’ exists between predicted and actual energy use, significantly influenced by occupant behaviour. This study explores energy-related behaviour in office buildings by integrating existing behavioural theories including the Theory of Planned Behaviour and the Self-determination Theory, and construct of habit and comfort. Data from an online survey were analyzed using principal component analysis, two-step cluster analysis, and descriptive statistics, identifying three behavioral clusters: ‘Cautious Saver’, ‘Compelling Dissatisfied’, and ‘Coherent Potent’. These clusters represent distinct energy-related behaviours. A Clustering-based Agent System (CAS) was then proposed to simulate the energy-related behaviours of these clusters, offering a dynamic and adaptive modelling framework. The study advocates for a comprehensive approach, integrating behavioural theories to provide insights for developing accurate occupant behaviour models. Abbreviations: BD: Biodiesel; BR: Blending Ratio; BSFC: Brake Specific Fuel Consumption; BTE: Brake Thermal Efficiency; CI: Compression Ignition; CM: Coating Material; CO: Carbon Monoxide; CT DEE: Coating Thickness Diethyl Ether; D-Gun: Detonation Gun; DOA: Degree of Adiabacity; EGT: Exhaust Gas Temperature; HC: Hydrocarbon; HVOF: High-velocity oxy-fuel; IC: Internal Combustion; LHR: Low Heat Rejection; NOx: Oxides of Nitrogen; PVD: Physical Vapour Deposition; SEM: Scanning Electron Microscopy; SI: Spark Ignition; TBC: Thermal Barrier Coating; TMF: Thermal Mechanical Fatigue; WCO: Waste Cooking Oil; XRD: X-ray Diffraction; YSZ: Yttria-Stabilised Zirconia

Hydropower potential of the Marsyangdi River and Bheri River basins of Nepal and their sensitivity to climate variables

Abstract. Understanding the hydrology of the Himalayan region and its response to current and future climate scenarios is crucial for identifying the region's future water availability for infrastructural development. For this, the hydropower potential and the impact of future change on the hydrology of the Bheri River basin (BRb) and the Marsyangdi River basin (MRb) were analysed. The Glacio-hydrological Degree-Day Model version 2.0 (GDM V2.0), developed in the PCRaster dynamic modelling framework, was used to simulate the river runoff. Geospatial tools and different criteria were used to assess topographic features, identify suitable places for run-of-river (ROR) hydropower development, and estimate power potential. Eight scenarios with various combinations of temperature and precipitation changes were used to assess the hydropower potential. Increases in temperature by 0.5 and 1 °C (assumed for the near-term and mid-term future) and changes in precipitation of ±10 % and ±20 %, respectively, are used to conduct a sensitivity analysis for the hydropower potential. A total of 116 and 83 suitable sites were identified, and 4242 and 2823 MW power potentials were estimated in the BRb and the MRb, respectively. All the sensitivity scenarios show an increase in hydropower production, except for one with a drier scenario and less precipitation. The integration of a geographic information system (GIS) and a hydrological model helps us to understand the hydrological response to climate variables and its impact on hydropower in the Himalayan region.

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

The STARTS model is a structural equation model that decomposes individual differences in a longitudinally assessed variable into a time-invariant stable component, a time-varying autoregressive component, and a time-point specific state component. Typically, the model is estimated with Maximum Likelihood in standard SEM software. Here, we show how different STARTS models (the single-indicator univariate and multivariate and the multiple-indicator univariate model) can be incorporated into the Dynamic Structural Equation Model framework. We use the data of an experience sampling study to illustrate the expositions and we provide codes to fit the models in Mplus. As the DSEM parameters are estimated with a Bayesian approach, we also report the results of three simulation studies in which we examined the statistical properties of the parameter estimates obtained in the DSEM framework. Finally, we discuss how to estimate variants of the STARTS model and we make suggestions for future applied and methodological research.

Synthesis of dynamic modelling framework and optimal control strategy for virtually coupled train sets with guaranteed safety

Virtual coupling represents a novel railway operational mechanism, wherein several trains share state information and adjust their behaviours based on control objectives, which include safety (collision avoidance) assurance and stability (uniform velocity and constant spacing between trains) maintenance. However, the conflict between safety and stability requirements becomes more apparent, as the closer spacing between trains. Moreover, the hybrid nature of the train platoon operation, which involves both discrete events and continuous dynamics, also poses a significant barrier to the control strategy synthesis. This paper presents the Virtually Coupled Train Sets (VCTS) dynamic modelling framework and strategy synthesis approach to address these problems. A Hybrid Markov Decision Process (HMDP) is adopted for relative longitudinal dynamics modelling of trains, considering the influence of the uncertainty dynamic of trains ahead. A Stackelberg game-based strategy synthesis algorithm is applied for the safe guarantee by overcoming the uncertainty, and a Q-learning method is used for stability strategy selection from the safe strategy. The results showed a 17.95% expansion in the safe state space when compared to the general relative braking scheme (which assumes maximal acceleration during the delay horizon). In a four-train tracking operation scenario, compared with Q-learning without safe constraints, our approach gives similar stability performance; compared with Q-learning with general relative braking safe constraints, our approach not only assurance safety but has a better stability performance.

Opinion dynamics beyond social influence

AbstractWe present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.

Assessment of a coupled electricity and hydrogen sector in the Texas energy system in 2050

Due to its ability to reduce emissions in the hard-to-abate sectors, hydrogen is expected to play a significant role in future energy systems. This study modifies a sector-coupled dynamic modeling framework for electricity and hydrogen by including policy constraints, carbon prices, and possible hydrogen pathways and applies it to Texas in 2050. The impact of financial policies, including the US clean hydrogen production tax credit, on required infrastructure and costs are explored. Due to low natural gas prices, financial levers are necessary to promote low-carbon hydrogen production as the optimized solution. The Levelized Costs of Hydrogen are found to be $1.50/kg in the base case (primarily via steam methane reformation production) and lie between $2.10 - 3.10/kg when production is via renewable electrolysis. The supporting infrastructure required to supply those volumes of renewable hydrogen is immense. The hydrogen tax credit was found to be enough to drive production via electrolysis.

Innovation in family businesses: Exploring the influence of entrepreneurial orientation and absorptive capacity on innovative capacity

Firms’ innovation positively affects their competitiveness and thus their financial performance. To bridge the research gap in the theoretical framework of dynamic capabilities and respond to the call for papers to explore new ways of analyzing innovation in family businesses, this study investigates how entrepreneurial orientation and absorptive capacity influence innovative capacity in family firms. Data from 156 family firms are analyzed using the theoretical framework of dynamic capabilities and structural equation modeling. The results reveal that both entrepreneurial orientation and absorptive capacity influence innovative capacity and that this influence is greater when both capabilities act together than when they act individually. This study confirms that entrepreneurial orientation and absorptive capacity are antecedents of innovative capacity. Moreover, entrepreneurial orientation has a greater influence on innovative capacity than absorptive capacity does.

Data-driven corporate growth: A dynamic financial modelling framework for strategic agility

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application

An Integrated Dynamic and Quality Modeling Framework for Batch Processes

This manuscript considers batch process operations and addresses the challenge of identifying a model that synergistically captures the dynamic input–output behavior of continuously measured variables along with the quality variables measured only at batch termination. To this end, an optimization-based framework is developed to identify one model that captures both the dynamics between the inputs and the continuously measured output variables, measurements of which are available at every time step, and the relation between the dynamic “state” information and the terminal quality measurements. Existing approaches either do not identify the dynamic and the quality model simultaneously, or they simply connect the whole trajectory of the process variables with the qualities and do not address the dynamic relationship between the inputs and the process variables. The improved modelling performance of the model obtained from this approach is demonstrated using data from a Uni-axial Rotational Molding process, and compared with existing modelling approaches.

KAN-ODEs: Kolmogorov–Arnold network ordinary differential equations for learning dynamical systems and hidden physics

Kolmogorov–Arnold networks (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a neural ordinary differential equation (ODE) framework, generalizing their use to the time-dependent and temporal grid-sensitive cases often seen in dynamical systems and scientific machine learning applications. The proposed KAN-ODEs retain the flexible dynamical system modeling framework of Neural ODEs while leveraging the many benefits of KANs compared to MLPs, including higher accuracy and faster neural scaling, stronger interpretability and generalizability, and lower parameter counts. First, we quantitatively demonstrated these improvements in a comprehensive study of the classical Lotka–Volterra predator–prey model. We then showcased the KAN-ODE framework’s ability to learn symbolic source terms and complete solution profiles in higher-complexity and data-lean scenarios including wave propagation and shock formation, the complex Schrödinger equation, and the Allen–Cahn phase separation equation. The successful training of KAN-ODEs, and their improved performance compared to traditional Neural ODEs, implies significant potential in leveraging this novel network architecture in myriad scientific machine learning applications for discovering hidden physics and predicting dynamic evolution.

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

Research on the framework and meteorological parameter optimization method of dynamic heating load prediction model for heat-exchange stations

Heat-exchange station is the key mid-link between heat source and heat users in district heating system, playing a role in distributing heat and regulating supply and demand. Accurate load prediction at the level of heat-exchange stations and then regulating the heat supply is the important step to optimize district heating system. Based on real and limited data, the framework of heat-exchange station load prediction method is established in this study, which includes 5 procedures ‘data preprocessing method, input variable selection, solar radiation substitution, prediction model establishment and model transplantation application’. The sliding box plot method was proposed for data preprocessing, and the effects of data preprocessing under the widths of 24 h, 48 h, 72 h, 96 h and 120 h were compared, with 48 h as the optimal sliding window width. Support Vector Regression (SVR) algorithm was used to establish a heating load prediction model for heat-exchange station in residential buildings. And parameter substitution was made for solar radiation data that cannot be obtained in weather forecasts. High accuracy was obtained with a MAPE of 5.80 % and RMSE of 0.18 GJ. The above model was transplanted and applied in three other heat-exchange stations of different building types. The MAPEs were 5.51 %, 7.79 % and 6.54 %, with corresponding RMSEs of 0.017 GJ, 0.04 GJ and 0.04 GJ, respectively, which all achieved good prediction results. The research results provide certain technical support for the operation and regulation of district heating systems.

A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology

Among a variety of virtual metrology models, dynamic generative latent variable models (DGLVMs) have proven to be an effective tool, owing to outstanding advantages in dealing with complicated data characteristics including temporal correlations, variable colinearities, high dimensionality, and process and measurement uncertainties. Presently, the vast majority of DGLVMs are developed upon the assumption that data are Gaussian-distributed, which is however, non-robust against outlying data and leads the DGLVMs to compromised performance, as the Gaussian distribution is susceptible to outliers that would skew the estimated parameters of DGLVMs. In view of this, this paper proposes a semi-supervised robust learning (SsRL) framework for the DGLVMs by designing an ad-hoc fully robust model structure that handles the outlying data by means of the heavy-tail properties of Student’s t distribution. In addition, the SsRL-DGLVM framework is initiated as a concrete model using probabilistic slow feature analysis (denoted RoSsPSFA) for which a rigorous training algorithm without resorting to any approximations is developed. The performance of the RoSsPSFA is thoroughly evaluated by both numerical and real-world industrial cases, showing the RoSsPSFA’s better immunity against outlying data and higher generalization accuracy.

Setting up a sovereign wealth fund to reduce currency crises

This paper assesses whether and how setting up a sovereign wealth fund has a buffer effect against currency crises. Using an innovative dynamic logit panel model framework and a unique dataset covering 34 emerging countries over the period 1989–2019, we empirically show that sovereign wealth funds reduce the occurrence of currency crises. This result is robust to different econometric specifications, alternative definitions of sovereign wealth funds, controlling for currency crisis risk factors, and income level sampling. Our findings have important implications for financial stability and for policymakers, who could further exploit the potential of sovereign wealth funds to better manage foreign exchange risks.