Dynamic Modeling Approach Research Articles

Unlocking synergies: Smart cities and the dynamic business model approach

Unlocking synergies: Smart cities and the dynamic business model approach

Knowledge and data fusion-driven dynamical modeling approach for structures with hysteresis-affected uncertain boundaries

Knowledge and data fusion-driven dynamical modeling approach for structures with hysteresis-affected uncertain boundaries

A two-step dynamic factor modelling approach for forecasting inflation in small open economies

We build a dynamic factor model to forecast inflation in a small open economy. The model is estimated with both market and survey data, and a unique two-step methodology to incorporate exogenous factors. Estimations with market data provide a better fit for in-sample and out-of-sample values of inflation. More importantly, our model outperforms univariate and estimated DSGE models, the more common approaches to inflation forecasting that perform well for advanced economies. Our findings, therefore, suggest that a dynamic factor modelling approach for a small open economy such as Jamaica can be a good alternative to the preferred methods for forecasting inflation in advanced economies.

Power Generation Optimization for Next-Generation Cruise Ships with MVDC Architecture: A Dynamic Modeling and Simulation Approach

The cruise industry is obliged by economic and environmental initiatives to pursue fuel-efficient solutions and lower ship exhaust emissions. The medium voltage DC (MVDC) distribution with intelligent power management has become a concept for next-generation onboard power systems as its energy-saving feature is to eliminate the frequency constraint and simultaneously optimize engine loads and speed in response to load variations. The incentive for this transition lies on one hand in the fuel efficiency consideration and the reduction of power losses from serial conversion stages. On the other hand, the DC-based technology has been conceived as high-power density design, thus significantly increasing the payload. This study investigates such potential benefits focusing exclusively on large cruise vessels. A highly representative model of the integrated power platform that incorporates all dynamic interactions from the ship hull and essential machinery typically installed on board cruise ships is proposed. The power management strategy also takes account of actual sea conditions and real-time operation requirements. The simulation results demonstrate that the optimization-based MVDC system is able to maximize the opportunity of search agents in finding optimum fuel efficiency areas throughout the scenario time. An analysis of the system structure weight and space reduction of the MVDC architecture is also performed through the utilization of more compact electrical distribution devices and very high power-dense combustion turbines.

A differentiable dynamic modeling approach to integrated motion planning and actuator physical design for mobile manipulators

AbstractThis paper investigates the differentiable dynamic modeling of mobile manipulators to facilitate efficient motion planning and physical design of actuators, where the actuator design is parameterized by physically meaningful motor geometry parameters. The proposed differentiable modeling comprises two major components. First, the dynamic model of the mobile manipulator is derived, which differs from the state‐of‐the‐art in two aspects: (1) the model parameters, including magnetic flux, link mass, inertia, and center‐of‐mass, are represented as analytical functions of actuator design parameters; (2) the dynamic coupling between the base and the manipulator is captured. Second, the state and control constraints, such as maximum angular velocity and torque capacity, are established as analytical functions of actuator design parameters. This paper further showcases two typical use cases of the proposed differentiable modeling work: integrated locomotion and manipulation planning; simultaneous actuator design and motion planning. Numerical experiments demonstrate the effectiveness of differentiable modeling. That is, for motion planning, it can effectively reduce computation time as well as result in shorter task completion time and lower energy consumption, compared with an established sequential motion planning approach. Furthermore, actuator design and motion planning can be jointly optimized toward higher performance.

Experimental parameter identification of scalable white and grey box jounce bumper models for ride comfort simulation

The design of a passenger car’s ride and handling goals requires an enormous amount of hardware and prototypes. To avoid time-consuming testing activities, one crucial trend is the virtualisation of suspension development. Another goal of the virtualisation is the determination of vehicle properties at an early stage of the development process. Highly accurate suspension models are required to transfer the full vehicle’s characteristics into simulation tools. Since the jounce bumper is an important component of the suspension system, modelling its functional behaviour is essential for virtualised chassis development. Therefore, two dynamic modelling approaches are derived and compared in this contribution. One approach is based on local linear model networks and thus can be seen as a grey box model. The other approach consists of white box rheological elements, which allow the physical interpretability of the model parameters. The comparison of the approaches is based on experimental parameter identification, which includes synthetic and real excitations recorded on representative roads. The advantages of the proposed approaches will be discussed in terms of their suitability for the virtual design of the driving behaviour.

Model-Based Adaptive Control of Bioreactors—A Brief Review

This article summarizes the authors’ experiences in the development and application of the General Dynamical Model Approach related to adaptive linearizing control of biotechnological processes. Special attention has been given to some original, innovative solutions in model-based process control theory: new formalization of biotechnological process kinetics, derivation and tuning of the general software sensor of the full kinetics of biotechnological processes, and a general algorithm for fully adaptive linearizing control with software sensors. These theoretical solutions are the basis of three control strategies—fully adaptive control of the main substrate, partially adaptive control of intermediate metabolite, and recognition and stabilization of the desired physiological state based on the proposed theoretical solutions. Each strategy is illustrated in different case studies. The advantages and limitations of each of them are identified and discussed. The derived algorithms for monitoring and controlling the considered biotechnological processes are realized and included in a software platform named Interactive System for Education in Modelling and Control of Bioprocesses (InSEMCoBio). The InSEMCoBio modules and their main functions are discussed. The effectiveness of the proposed control strategies (achieving maximum productivity) has been proven through a series of simulation investigations of the considered case studies.

The role of macro institutional factors in determining types of entrepreneurial start-ups: a longitudinal panel study

Purpose What shapes entrepreneurs’ intention to allocate entrepreneurial effort towards different types of entrepreneurial start-ups? Grounded in a “national business systems” approach, the purpose of this paper is to examine the impacts of institutional dimensions on Schumpeterian and Kirznerian entrepreneurship. Design/methodology/approach Drawing upon a global context comprising 82 countries over the period between 2007 and 2018, this research applies a dynamic panel modelling approach, namely, the dynamic panel generalised method of moments estimator. This estimator allows to account for unobserved country-specific heterogeneity and to address endogeneity constraints that might occur between institutions and entrepreneurship. Findings The findings reveal that there are positive relationships between financial capital and both Schumpeterian and Kirznerian entrepreneurship. Educational capital positively affects the allocation of entrepreneurial efforts towards opportunity-based entrepreneurial activities. Moreover, institutional regulatory conditions could hinder the allocation of entrepreneurs’ resources into a Schumpeterian while facilitating the allocation of resources into a Kirznerian type of venture. Finally, a higher level of corruption promotes innovative entrepreneurial activities (i.e. a Schumpeterian type of venture) and leads to constraints on Kirznerian entrepreneurship. Practical implications The research findings demonstrate the significance of the macro environment for enacting and implementing policies to reap the benefits of different types of start-ups. It suggests different political actions are needed to motivate highly qualified individuals to engage in Schumpeterian and Kirznerian entrepreneurship, rather than focusing purely on enhancing a country’s overall start-up rates. Originality/value By acknowledging the multidimensional nature of entrepreneurship, this research provides greater theoretical exposition and empirical support for the role played by macro-level institutions in determining types of entrepreneurship. It reveals the important role played by macro institutional conditions in influencing choices about different types of start-ups and gives rise to the multidimensional nature of entrepreneurship.

Accelerated 3D CFD modeling of multichannel flat grooved heat pipes

Flat grooved heat pipes (HPs) have become essential in advanced thermal management solutions across various engineering applications. Modeling these devices, especially multichannel flat grooved HPs, involves significant challenges due to complex phenomena such as phase-change heat transfer and free-surface flow, requiring substantial computational resources, time and expertise. These constraints often limit the full exploration and optimization of HPs’ potential in diverse applications. To address this gap, an accelerated 3D computational fluid dynamics (CFD) modeling approach is presented in this study. This novel method begins with a detailed 3D modeling of a single groove, developed using kinetic theory and facilitated by CFD software. The results from this model are then applied as boundary conditions to simulate the entire HP in a multichannel configuration. The importance of this methodology is further highlighted by the alignment of simulation results with experimental observations. The approach significantly enhances computational efficiency by reducing the number of iterations by 10% and computational time by 80%, resulting in a five-fold speed-up. The methodology enables accelerated, comprehensive modeling of multichannel variations and delivers critical insights for optimizing the design of multichannel flat grooved HPs for various engineering applications.

Type-3 fuzzy logic and Lyapunov approach for dynamic modeling and analysis of financial markets

Chaos theory offers a new way to investigate variations in financial markets data that cannot be obtained with traditional methods. The primary approach for diagnosing chaos is the existence of positive small Lyapunov views. The positive Lyapunov index indicates the average instability and the system's chaotic nature. The negativity indicates the average rate of non-chaoticness. In this paper, a new approach on basis of type-3 fuzzy logic systems is introduced for modeling the chaotic dynamics of financial data. Also, the attracting dimension tests and the Lyapunov views in the reconstructed dynamics are used for examinations. The simulations on case-study currency market show the applicability and good accuracy of the suggested approach.

Modeling Determinants of Profitability in Ethiopia Manufacturing Companies: A Dynamic Panel Model Approach

Background: Profitability is the primary measure of the overall success of company. The analysis of profitability ratios is important for the shareholders, creditors, prospective investors, manufacturers and government alike. Objective: The objective of this study was fitted a dynamic panel regression model that efficiently fit the data and further to identify the determinants contributing significantly to profitability in Ethiopia. Methods: This study was conducted on secondary data of 32 sample manufacturing companies collected from the audited financial statements of large tax payers’ office and national bank of Ethiopia. The study was covered a period of seven years from 2011 to 2017. The panel unit root test of Levin-Lin-Chu tests was made for each variable and applied first difference transformation for the variables that had unit root. A dynamic panel regression model was utilized for data handling technique using generalized method of moments (GMM) estimation. Results: We compared the results when one unit increase in lagged profitability, managerial efficiency, capital intensity, GDP, exchange rate and one ratio decrease in leverage, ceteris paribus, turn out were found to increase the profitability of manufacturing companies by around 0.69, 0.179, 4.52E-06, 3.844, 0.04 and 0.393 ratio, respectively. The model reveled that previous profitability, leverage, capital intensity, managerial efficiency, GDP and exchange rate had a statistically significant (P<0.05) effect on companies’ profitability in Ethiopia. Conclusion: As per our findings, the manufacturing companies should minimize leverage financing from its capitals and should emphasize the management of appropriate financing to increase profitability. Finally, we recommended that policy makers should coming up with better policies on improvement of profitability.

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Data-driven modeling of multi-stable origami structures: Extracting the global governing equation and exploring the complex dynamics

In recent years, multi-stable origami structures have garnered increasing attention for their applications in dynamic scenarios such as robotic arm motions, impact energy absorption, and spectrum gap regulation. Understanding the intricate working mechanisms and exploring the rich dynamics of these structures necessitate the development of dynamic models. However, existing dynamic modeling methods for origami structures are often cumbersome, and the resulting dynamic models often lack interpretability. To overcome these limitations, we propose a novel data-driven dynamic modeling approach based on B-spline Galerkin method and subset selection strategy. This approach directly captures the dynamics of multi-stable origami structures using measured data, eliminating the need for reliance on empirical or prior knowledge. To validate the effectiveness of the proposed approach, we first evaluate it on the Duffing system, which has explicit expressions, successfully reconstructing the governing equation. Subsequently, we apply this method to the dynamic modeling of the origami ball structure with tri-stability and the multi-cell stacked Miura-origami (SMO) structure with high dimensionality through simulation, showing favorable results. Finally, using experimental data collected from a bi-stable SMO structure prototype, we employ the proposed method to obtain a global model that can accurately predict different dynamic behaviors over a broad range of excitation frequencies, including intra-well periodic vibrations, inter-well periodic vibrations, and inter-well chaotic vibrations. Overall, our method showcases outstanding efficacy in formulating interpretable, manageable, and comprehensive dynamic models. It plays a pivotal role in delving into the intricate dynamics of multi-stable origami structures.

Dynamic Modeling and Experimental Modal Analysis for the Central Rod-Fastened Rotor With Hirth Couplings Based on Fractal Contact Theory

Abstract The central rod-fastened rotor of gas turbine exhibits pronounced noncontinuous characteristics due to the large number of contact interfaces between the compressor and turbine disks. It is necessary to establish an accurate dynamic modeling method for the central rod-fastened rotor that fully considers the contact surface effect. In this work, the contact behavior of the rough surface is characterized by the fractal theory. The normal and tangential contact stiffness models are developed, and the influence of fractal parameters is discussed. Besides, the finite element model for the central rod-fastened rotor is established by developing an improved contact element considering the equivalent stiffness segment of Hirth couplings. Finally, the proposed model is verified by conducting the modal testing and measuring the first four modes of natural frequencies and modal shapes of the central rod-fastened rotor. The results show that the numerical results are in good agreement with the experimental ones, and the fractal contact model can effectively predict the connection stiffness of Hirth couplings, which in turn improves the simulation accuracy for the modal characteristics of the central rod-fastened rotor and provides a dynamic modeling approach with high efficiency and less computational complexity.

An effective nonlinear dynamic formulation to analyze grasping capability of soft pneumatic robotic gripper

ABSTRACT Soft pneumatic robotic grippers have found extensive applications across various engineering domains, which prompts active research due to their splendid compliance, high flexibility, and safe human-robot interaction over conventional stiff counterparts. Previously simplified rod-based models principally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives, whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation behaviors. To address this, based on absolute nodal coordinate formulation (ANCF), comprehensively allowing for structural complexity, geometric, material and boundary nonlinearities, and incorporating Coulomb’ friction law with a multiple-point contact method, we put forward an effective nonlinear dynamic modeling approach for exploring grasping capability of soft gripper. Moreover, we solved the established dynamic equations using Generalized-α scheme, and conducted thorough numerical simulation analysis on a three-jaw soft pneumatic gripper (SPG) in terms of grasping configurations, displacements and contact forces. The proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets, whose effectiveness and precision were analyzed theoretically and verified experimentally, which may shed new light on devising and optimizing other multifunctional SPGs.

Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling

This study explores the potential of dynamic, machine learning (ML)-based modeling to enhance students’ argumentation skills—a crucial component in education and professional success. Traditional educational tools often rely on static modeling, which does not adapt to individual learner needs or provide real-time feedback. In contrast, our research introduces an innovative ML-based system designed to offer dynamic, personalized feedback on argumentation skills. We conducted three empirical studies comparing this system against traditional methods such as scripted and adaptive support modeling. Our results show that dynamic behavioral modeling significantly improves learners’ objective argumentation skills across domains, outperforming all established methods. The results further indicate that, compared with adaptive support, the effect of the dynamic modeling approach holds across complex (large effect) and simple tasks (medium effect) and supports learners with lower and higher expertise alike. This research has important implications for educational policy and practice; incorporating such dynamic systems could transform learning environments by providing scalable, individualized support. This would not only foster essential skills but also cater to diverse learner profiles, potentially reducing educational disparities. Our work suggests a shift toward integrating more adaptive technologies in educational settings to better prepare students for the demands of the modern workforce.

Future Changes in Regional Tropical Cyclone Wind, Precipitation, and Flooding Using Event‐Based Downscaling

AbstractUnderstanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Optimization of Aviation Biofuel Development as Sustainable Energy Through Simulation of System Dynamics Modeling

This study aims to optimize the development of aviation biofuel as a sustainable energy source by simulating system dynamics modeling. This study is based on the System Dynamics modeling approach, which is a set of conceptual tools designed to understand the structure and dynamics of complex systems. This study used the system dynamics method specifically designed to analyze complex systems. It has been applied to various sustainability-related issues, including urban area sustainable development modeling, sustainability of water resources, environmental management, and sustainable urbanization. The result obtained using the quantitative modeling showed that the contribution of aviation biofuel to flight intensity in Indonesia is still insignificant. The practical implications of this study are that palm oil has the potential to be a viable raw material for aviation biofuel production in Indonesia, and implementing policies to mitigate negative consequences and optimize land use for aviation biofuel fuel production can contribute to sustainable urban development. The originality of this study lies in its use of System Dynamics modeling to analyze the potential of palm oil as a raw material for aviation biofuel production and identify the various social, economic, environmental, and technological factors that impact it.

Human movement and environmental barriers shape the emergence of dengue

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection.

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.