System Dynamics Method Research Articles

Quantum nature of reactivity modification in vibrational polariton chemistry.

In this work, we present a mixed quantum-classical open quantum system dynamics method for studying rate modifications of ground-state chemical reactions in an optical cavity under vibrational strong-coupling conditions. In this approach, the cavity radiation mode is treated classically with a mean-field nuclear force averaging over the remaining degrees of freedom, both within the system and the environment, which are handled quantum mechanically within the hierarchical equations of motion framework. Using this approach, we conduct a comparative analysis by juxtaposing the mixed quantum-classical results with fully quantum-mechanical simulations. After eliminating spurious peaks that can occur when not using the rigorous definition of the rate constant, we confirm the crucial role of the quantum nature of the cavity radiation mode in reproducing the resonant peak observed in the cavity frequency-dependent rate profile. In other words, it appears necessary to explicitly consider the quantized photonic states in studying reactivity modification in vibrational polariton chemistry (at least for the model systems studied in this work), as these phenomena stem from cavity-induced reaction pathways involving resonant energy exchanges between photons and molecular vibrational transitions.

The Complex Interrelationships of the Risk Factors Leading to Hamstring Injury and Implications for Injury Prevention: A Group Model Building Approach

There is a gap in the literature regarding the complex interrelationships among hamstring injury (HI) risk factors. System dynamics (SD) modeling is considered an appropriate approach for understanding the complex etiology of HI for effective injury prevention. This study adopted the SD method and developed a causal loop model (CLD) to elucidate the intricate relationships among HI risk factors. This is performed by combining literature evidence and insights from expert stakeholders through a group model building (GMB) approach. The GMB methodology facilitated the identification of sixty-five critical factors influencing the HI risk, revealing the dynamic interplay between factors. Stakeholder engagement underscored the importance of previous injury characteristics (level of influence of previous injury, severity of previous injury, quality and size of scar tissue) and the quality of rehabilitation. HI-CLD revealed that many factors had indirect effects on HI risk. The HI-causal loop model establishes a foundation for a future stock and flow quantitative SD model aiming to advance HI prevention strategies through an interdisciplinary collaborative effort. These findings underscore the complexity of HI prevention, necessitating a holistic approach that integrates the views of diverse professional expertise. Appropriate inter-professional collaboration and continuous athlete screening are important for effective injury prevention strategies.

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

Using system dynamics to support a functional exercise for pandemic preparedness and response

AbstractIn pandemic preparedness and response, a Functional Exercise (FX) is used to simulate a situation as close to a real‐life event as possible without the deployment of resources. Participants are drawn from public health emergency operations centres, and work through a scenario script to test possible responses to a novel pathogen outbreak. This paper summarises the role of system dynamics modelling in the design and implementation of a functional exercise, which involved the Dutch and German national public health institutes in March 2023. The findings confirm the value of the system dynamics method in integrating disease and hospital models, and also highlights how well the method aligns with modern software development processes. The paper concludes with a discussion of what worked well, and presents areas for future enhancements of management flight simulators to support functional exercises. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Forecasting urban passenger transportation emissions: a green and new energy approach

ABSTRACT Passenger transportation significantly contributes to urban carbon dioxide emission. Decreasing these emissions is essential to achieving low-carbon development. This research focused on Xi’an City to enhance previous research works by considering factors such as road greening, online ride-hailing, and use of vehicles with new energy sources. Using system dynamics method, a carbon dioxide emission prediction model was developed for urban passenger transportation in Xi’an. The developed model estimated total emissions from 2012 to 2021 and projected carbon dioxide emission reduction plans through six different scenarios for the time period of 2023 to 2032. The analysis identified private cars as the main contributors to carbon emission in urban passenger transportation. Single scenario simulations indicated that passenger traffic in Xi’an would not achieve carbon emission peak by 2030, but multi-scenario simulations showed it would. Specific recommendations for carbon emission reduction in urban passenger transportation were provided, focusing on travel patterns, energy sources, and promotion of green initiatives.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

Sustainable Women Entrepreneurship through Microfinance: A System Dynamic Approach

An Indian woman business owner makes a substantial contribution to the nation's economic growth. Women play a significant role in society and contribute to the general growth of the economy. Regretfully, despite the modernity of society, women continue to face several obstacles and are prohibited from conducting business in many regions of the nation. The current research helps to understand the problems and constraints faced by women entrepreneurs. Additionally, this study contributes to the long-term mitigation and surmounting of obstacles facing female entrepreneurs. This study aims to use microfinance to create sustainable women's businesses. A system dynamic method is also used to investigate the various positive and negative interactions between the influencing variables that determine the achievement of women entrepreneurs. To reduce the economy's social, economic, market, and financial vulnerabilities and support women entrepreneurs' long-term capacity building through microfinance.

MODEL SISTEM DINAMIK UNTUK KEBERLANJUTAN DANAU BATUR, BATUR UNESCO GLOBAL GEOPARK, BALI

This research aims to analyze the best scenario for conserving Lake Batur, Batur UNESCO Global Geopark (BUGG), Bali. Using the system dynamics method, key factors in the management of Lake Batur can be identified. Based on modeling results, it is known that the sustainability of Lake Batur is strongly influenced by aspects of land use, the environment, the economy, and the social culture of the community. The main leverage factors in supporting the preservation of Lake Batur are law enforcement and supervision, government assistance, as well as training and outreach to the community. The analysis of the sensitivity value of the optimistic/moderate ratio shows that the optimistic scenario is the best option to reduce the lake pollution index to 0.86 (still below the 1 threshold). Increasing community income per capita in this area in 2045 is estimated to reach Rp87.992.747,76/year, with predictions of 9,312 floating net cage plots owned by 2,592 entrepreneurs. The predicted number of plots is 14.90 ha, which is still below 1% of the area of Lake Batur. Keywords: System Dynamics; Sustainability; Lake Batur; BUGG

Evaluation method investigation of public decontamination time in off-site nuclear emergency under the influence of human factors

Decontamination is an important means to reduce the personnel radioactive contamination under sever nuclear accidents. Public decontamination time estimation with the process simulation is helpful for emergency organizations to improve efficiency. To date, most studies focus on improving personal decontamination technology and decontamination strategies, while research on the overall decontamination process and time estimating is limited. Due to the complex population structure, the unknown contaminated proportion and the complicated psychological emotions, it is hard to quantify the decontamination process and calculate the decontamination time with numerous procedures and diverse facility configurations. Combined with public contaminated proportion, crowd structure and waiting psychology, a public decontamination model based on queuing theory and system dynamics method is established for offsite nuclear emergency to simulate the decontamination process and give an estimate of decontamination time. A hypothetical nuclear leakage accident of Changjiang Nuclear Power Plant in Hainan, China is conducted to simulate the established model. The results show the working status of different facilities at different stages and the changes in waiting queues at different times, which can effectively calculate the duration time of each link and the overall public decontamination progress. A plan for adding devices is provided to meet the decontamination time requirements and control the number of waiting queues within the specified threshold efficiently. The method proposed in this paper could help nuclear emergency decision-makers estimate the public decontamination time, optimize site configuration plans, give guidance on public decontamination procedures and prepare for the subsequent nuclear emergency evacuation.

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.

A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons.

Recordings from pre-Bötzinger complex neurons responsible for the inspiratory phase of the respiratory rhythm reveal a ramping burst pattern, starting around the time that the transition from expiration to inspiration begins, in which the spike rate gradually rises until a transition into a high-frequency burst occurs. The spike rate increase along the burst is accompanied by a gradual depolarization of the plateau potential that underlies the spikes. These effects may be functionally important for inducing the onset of inspiration and hence maintaining effective respiration; however, most mathematical models for inspiratory bursting do not capture this activity pattern. Here, we study how the modulation of spike height and afterhyperpolarization via the slow inactivation of an inward current can support various activity patterns including ramping bursts. We use dynamical systems methods designed for multiple timescale systems, such as bifurcation analysis based on timescale decomposition and averaging over fast oscillations, to generate an understanding of and predictions about the specific dynamic effects that lead to ramping bursts. We also analyze how transitions between ramping and other activity patterns may occur with parameter changes, which could be associated with experimental manipulations, environmental conditions, and/or development.

FINANCING MODEL OF SHARIA ECONOMY-BASED COFFEE AGROINDUSTRY USING DYNAMIC SYSTEM SIMULATION IN BANDUNG DISTRICT

West Java Province is one of the producers of export quality Arabica coffee, namely Java Preanger Coffee. The problem that often arises in the agricultural sector is that most farmers do not have the funds to make capital. Another problem in agriculture is capital. This can cause crop failure due to a lack of capital so the process of agriculture is delayed and hampered. The aim of the Islamic financing modelling research is to determine the relationship between variables in the Islamic economic-based financing system as well as a comparison between the application of the concept of profit-sharing margins for mudharabah and musyarakah between farmers and Muhammadiyah Branch Managers (PCM) using the system dynamics method using the Powersim Software. System dynamics are defined to address large-scale and complex systems and understand the relationships among behavior in a given system over time and the underlying structures and decision rules. The sharia economy-based coffee agroforestry financing model results in the application of profit sharing that reflects more justice. The principle of profit sharing carried out by PCM is beneficial for both parties who have achieved the principle of justice, by avoiding things that are unlawful. This can be seen from the results of the profits and losses are not on one side. The simulation results can be used as a consideration for decision making for financing coffee agroforestry in Bandung Regency.

China's future energy vision: Multi-scenario simulation based on energy consumption structure under dual carbon targets

In the face of global environmental problems, China has advanced a “dual-carbon" target, and the proportion of non-fossil energy consumption will be more than 25 % in 2030 and more than 80 % in 2060. Considering this goal as the starting point, this study applies the system dynamics method and combines different types of sectors and energy consumption structures to model China's energy consumption system. Through multi-scenario simulations of different energy consumption structures, the study finds that: (1) the growth rate of energy consumption and energy consumption structure are the main factors affecting carbon emissions; (2) industrial carbon emissions follow the changes in total carbon emissions, and the industry is the most important and challenging sector for solving the problem of carbon emissions; (3) under the premise of optimizing the energy consumption structure, China can achieve peak carbon in 2030, and to achieve carbon neutrality in 2060; (4) under the inertia of social development and energy structure, the energy demand of the tertiary sector is expanding, and natural gas is an unquestionable choice for China as a transitional energy source.

On the persistence and related mechanisms for day–night compound humid heat extremes in the Northern Hemisphere

Hot extremes pose adverse impacts on human health and ecosystem, leading to aggravated damage when they combine high-humidity and occur in the both daytime and nighttime. Although considerable studies have focused on hot extremes, understandings about day–night Compound humid heat (quantified by Moist Enthalpy) Extremes (CMEEs) are still lacking. This work investigates their frequency, linear trends and temporal persistence in the Northern Hemisphere, and two typical vulnerable regions are selected as Central Europe (CE) and the Arabian Peninsula (AP), both exhibiting high frequency and positive trends, but with contrasting persistence, which is quantified by the bivariate Dynamical System method. Results reveal their regional dependence and physical processes: the dual importance of sensible and latent heat in CE is attributed to the combination of an anomalous anticyclone and evaporation, whereas the dominance of latent heat in AP is largely owing to the convective precipitation. CMEEs in AP can be further divided into two groups with distinct persistence, and this disparity actually depends on the preceding precipitation duration and its associated water vapor supply.

Data-driven reconstruction of chaotic dynamical equations: The Hénon–Heiles type system

In this study, the classical two-dimensional potential VN=12mω2r2+1NrNsin(Nθ), N∈Z+, is considered. At N=1,2, the system is superintegrable and integrable, respectively, whereas for N>2 it exhibits a richer chaotic dynamics. For instance, at N=3 it coincides with the Hénon–Heiles system. The periodic, quasi-periodic and chaotic motions are systematically characterized employing time series, Poincaré sections, symmetry lines and the largest Lyapunov exponent as a function of the energy E and the parameter N. Concrete results for the lowest cases N=3,4 are presented in complete detail. This model is used as a benchmark system to estimate the accuracy of the Sparse Identification of Nonlinear Dynamical Systems (SINDy) method, a data-driven algorithm which reconstructs the underlying governing dynamical equations. We pay special attention at the transition from regular motion to chaos and how this influences the precision of the algorithm. In particular, it is shown that SINDy is a robust and stable tool possessing the ability to generate non-trivial approximate analytical expressions for periodic trajectories as well.

A multilevel system cross-game dynamics approach for carbon emission governance strategy

The relationships within the carbon emissions game are diverse and intricate, encompassing both horizontal games between regions and vertical games involving governments, enterprises, and the public. To explore the complex game relationships among carbon emission entities, this study integrated horizontal and vertical games and applied the system dynamics method to construct a multilevel and multiagent cross-game model, analyzing the changing trends of strategies among different regional governments, enterprises, and the public. The research reveals that there is free-riding behavior in the governance of carbon emissions among regions. However, this free-riding behavior can lead to a virtuous cycle. The attitude of the government plays a crucial role in determining whether businesses engage in carbon emission management. Increasing government regulatory efficiency does not alter the outcome of the game, but it can reduce the time required to reach Nash equilibrium. The study enhances the simulation of carbon emission game theory and provides a reliable reference for government regulation and low-carbon development in businesses.

Development of an Evaluation System for Intelligent Construction Using System Dynamics Modeling

Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry.

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya’an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

Exploring the Evolution Trend of China’s Digital Carbon Footprint: A Simulation Based on System Dynamics Approach

The rapid growth of the digital economy has heightened concerns over its environmental impacts, particularly in terms of carbon dioxide emissions. In contrast to previous studies that focus on the positive effects of digital technology on reducing carbon emissions, this paper provides a detailed analysis of the various factors that influence digital economy carbon emissions and their interrelationships, using the system dynamics method to simulate and predict China’s future digital economy carbon emission baseline from 2016 to 2046. Four different scenarios were established by adjusting parameters for the percentage of the digital economy, e-waste growth rate, and data center power consumption. The simulation results indicate the following: (1) The baseline scenario shows China’s digital economy carbon emissions peaking at 1.9045 billion tons in 2041 after an initial increase and subsequent decrease. (2) Single-policy simulations indicate that changing the digital economy’s scale and e-waste growth independently leads to peak emissions of 1.9205 billion tons and 1.5525 billion tons, respectively. Adjusting data center power consumption has a greater impact, increasing the peak emissions to 2.1675 billion tons, a 13.82% rise from the baseline. (3) Under the comprehensive regulation scenario, emissions peak in 2040 at 2.0813 billion tons, considering the interactions between the digital economy, data center power, and e-waste. Based on the findings, we recommend fostering innovation in the digital industry, enhancing the e-waste treatment process, strategically developing digital infrastructure, and exploring effective carbon reduction strategies for the digital economy aimed at supporting China in achieving its dual-carbon goals.