System Dynamics Research Articles

Study on Household Investment Decision of Household Photovoltaic Project Promotion -- Based on Inclusive Finance Perspective

Considering the lack of research on households' investment decisions in promoting photovoltaic projects in existing studies, this study constructs a four-party evolutionary game model based on local governments, financing institutions, photovoltaic enterprises and households from the perspective of inclusive finance. In addition, according to the influence of irrational psychological factors on the decision-making outcome of each interested party in decision-making, this study innovatively introduces regret theory to improve the evolutionary game model. Then, each subject' evolutionary stabilisation strategies and system equilibrium points are analysed explicitly using the perceived benefit function and the replicated dynamic differential equation. Finally, combined with the system dynamics method, the theoretical derivation results are verified by parameter assignment, and the sensitivity of each subject to multiple influencing factors is explored. The results show that: (1) under the current market condition, the system is stabilised in the strategy combination of Case I (subsidy, conventional finance, non-promotion, loan to build), but the ideal state can be achieved through the refinement and adjustment of policies. And there is a mutual influence between the subjects. (2) The regret-elation perception psychology of each subject has a non-negligible influence on their decision-making behaviour. The combination of strategies in Case I is a conservative decision by the subjects. (3) A variety of factors influences the behavioural decisions of each subject, and the amount of rent that photovoltaic enterprises can provide is a crucial factor affecting households' investment decisions. (4) Local governments incentives and penalties play a key incentive role in promoting the active participation of financing institutions and photovoltaic enterprises in the promotion endeavour. This study provides practical guidance and policy insights for promoting the diffuse use of renewable energy and the promotion of household photovoltaic projects while enriching the applied research by combining regret theory and evolutionary game theory.

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

Identifying the active ingredients in payment for performance programmes using system dynamics modelling

Payment for performance (P4P) is not a uniform intervention, with programme effect dependent on several interconnected factors. In this study, a system dynamics model was developed to explore the pathways to improved outcomes and how changes in the design, implementation and context of a P4P programme affected maternal and child health (MCH) service delivery outcomes in Tanzania. A previously developed causal loop diagram of the programme effects was used to inform model development, with further data sources (including an impact evaluation of programme, health surveys, stakeholder feedback and relevant literature) used to build the model. A number of pathways were identified to improved services under P4P, with increased availability of drugs underpinning the content of care outcome (intermittent preventative treatment during ANC), which together with increased supervision, enhanced health worker motivation. This in turn increased perceived quality of care at the facility which improved the coverage of services outcome (facility-based deliveries), and with increased outreach, increased awareness of services also boosted demand. Minor delays in payment reduced provider purchasing power for medicines, with severe delays driving erosion of provider trust and motivation for programme participation. Allocating a larger share of funds for facility operations can enhance performance effects, particularly for those services that rely on efficient drug administration. Contextual factors including limited baseline provision of essential medications, lower community awareness of facility services and dispersed/distant populations reduced programme effect. This paper demonstrates the feasibility and the potential of such models to inform the design of effective health system interventions.

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

T Regulatory Cell Subsets Do Not Restore for One Year After Acute COVID-19.

COVID-19, caused by SARS-CoV-2, triggers a complex immune response, with T regulatory cells (Tregs) playing a crucial role in maintaining immune homeostasis and preventing excessive inflammation. The current study investigates the function of T regulatory cells during COVID-19 infection and the subsequent recovery period, emphasizing their impact on immune regulation and inflammation control. We conducted a comprehensive analysis of Treg subpopulations in peripheral blood samples from COVID-19 patients at different stages: acute infection, early convalescence, and long-term recovery. Flow cytometry was employed to quantify Tregs including "naïve", central memory (CM), effector memory (EM), and terminally differentiated CD45RA+ effector cells (TEMRA). Additionally, the functional state of the Tregs was assessed by the expression of purinergic signaling molecules (CD39, CD73). Cytokine profiles were assessed through multiplex analysis. Our findings indicate a significant decrease in the number of Tregs during the acute phase of COVID-19, which correlates with heightened inflammatory markers and increased disease severity. Specifically, we found a decrease in the relative numbers of "naïve" and an increase in EM Tregs, as well as a decrease in the absolute numbers of "naïve" and CM Tregs. During the early convalescent period, the absolute counts of all Treg populations tended to increase, accompanied by a reduction in pro-inflammatory cytokines. Despite this, one year after recovery, the decreased subpopulations of regulatory T cells had not yet reached the levels observed in healthy donors. Finally, we observed the re-establishment of CD39 expression in all Treg subsets; however, there was no change in CD73 expression among Tregs. Understanding these immunological changes across different T regulatory subsets and adenosine signaling pathways offers important insights into the disease's pathogenesis and provides a broader view of immune system dynamics during recovery.

Untangling Circularity: Transitioning to a Regional Circular Economy in the Clothing and Textile Sector in England

The transition to a Circular Economy (CE) presents a paradigm shift, emphasizing sustainability, resource efficiency, and waste reduction. The existing literature extensively covers the principles and benefits of CE, but there is a notable gap in understanding how these practices are implemented at a local level. Specifically, there is a lack of detailed insights into the localized challenges, adaptation processes, and contextual factors influencing the adoption of circular practices. Additionally, the implications for different types of stakeholders and the effectiveness of localized strategies remain underexplored. By employing causal loop diagrams to depict system dynamics, interactions, and connections inherent in the transition to a CE, this research delves into the significance of understanding the complexities of circularity within a specific localized context. We draw qualitative data from semi-structured interviews and workshops with participants from key stakeholders within the textile and clothing sector in the Southwest of England. We find that legislation has a different impact on multinational corporations (MNCs) and small and medium-sized enterprises (SMEs) in their CE engagement. We also find that, while the flow of materials from MNCs to SMEs fosters regional innovation and knowledge-sharing, it does not always facilitate inter-organizational learning. Finally, both MNCs and SMEs recognize the CE’s potential for job creation, which highlights the need for more effective knowledge exchange strategies between businesses of different sizes. The study concludes that balancing CE principles with profit-driven pressures remains a significant challenge for both those founded on circular principles and those who are gradually incorporating CE principles into their organisation.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

Exploring the Digital Transformation of Generative AI-Assisted Foreign Language Education: A Socio-Technical Systems Perspective Based on Mixed-Methods

This study investigates the complex dynamics and impacts of generative AI integration in foreign language education through the lens of the Generative AI-assisted Foreign Language Education Socio-Technical System (GAIFL-STS) model. Employing an integrated mixed-methods design, the study combines qualitative case studies and hybrid simulation modeling to examine the affordances, challenges, and implications of AI adoption from a multi-level, multi-dimensional, and multi-stakeholder perspective. The qualitative findings, based on interviews, observations, and document analyses, reveal the transformative potential of generative AI in enhancing language learning experiences, as well as the social, cultural, and ethical tensions that arise in the process. The quantitative results, derived from system dynamics and agent-based modeling, provide a systemic and dynamic understanding of the key variables, feedback loops, and emergent properties that shape the trajectories and outcomes of AI integration. The integrated findings offer valuable insights into the strategies, practices, and policies that can support the effective, equitable, and responsible implementation of AI in language education.

Prediction of strain level phage-host interactions across the Escherichia genus using only genomic information.

Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage-host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage-bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage-host specificity and supports the use of predictive algorithms in phage therapy.

Legionella community dynamics in a drinking water distribution system: Impact of residual chlorine depletion

This study investigated the occurrence of Legionella spp. in a chlorinated drinking water distribution system (DWDS), focusing on their community compositions and association with physicochemical water quality. Water samples were collected throughout the DWDS, covering from the treated water reservoir to distal ends. Although Legionella spp. genes were not detected at the reservoir, their abundance dramatically increased along the distribution network, reaching up to 4.4 log copies/L at distal sites. The Legionella communities were further characterized by high-throughput amplicon sequencing targeting the genus-specific 16S rRNA gene. The results revealed a diverse Legionella community, including amplicon sequence variants with high similarity (> 99 %) to potentially pathogenic species such as L. drozanskii and L. pneumophila, albeit at low levels. Moreover, Legionella community diversity increased significantly along the distribution system, leading to distinct community compositions at distal sites. Importantly, decay of residual chlorine concentration was identified as a key factor both in increasing the Legionella gene levels and shaping the community structure. Overall, this study underscores the importance of preventing pipe corrosion and maintaining adequate disinfectant residuals to minimize Legionella regrowth in DWDS.

The Impact of White Noise on Chaotic Behavior in a Financial Fractional System with Constant and VariableOrder: A Comparative Study

In this study, we investigated the impact of white noise on the chaotic dynamics of a financial system with Caputo fractional derivatives of constant- and variable-order. We solve thesefractional financial systems numerically. We analyze the chaotic behavior of these fractional-order hyperchaotic systems through simulations, study the effects of white noise on chaotic dynamics, and provide a comparison between fractional hyperchaotic systems of constant and variable orders. The study reveals that white noise can either amplify or suppress chaos, with significant differences observed between the constant- and variable-order cases. We obtained numerous intriguing graphical findings for the model by evaluating various scenarios. The findings provide useful information for better understanding financial market dynamics in the presence of noise.

Synchronization of Multi-Agent Systems Composed of Second-Order Underactuated Agents

The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply the exact feedback linearization to the agents. This transformation divides their dynamics into a linear observable part and a non-observable part. It is shown that consensus of the linearizable parts of the agents implies consensus of the entire multi-agent system. To achieve the consensus of the original system, the inverse transformation of the exact feedback linearization is applied. However, its application causes changes in the dynamics of the multi-agent system; a way to mitigate this effect is proposed. Two examples are presented to illustrate the efficiency of the proposed synchronization algorithm. These examples demonstrate that the synchronization error decreases faster when the proposed method is applied. This holds not only for the states constituting the linearizable dynamics but also for the hidden internal dynamics.

Multiple crossings during dynamical symmetry restoration and implications for the quantum Mpemba effect

Abstract Local relaxation after a quench in 1D quantum many-body systems is a well-known and very active problem with rich phenomenology. Except in pathological cases, the local relaxation is accompanied by the local restoration of the symmetries broken by the initial state that are preserved by unitary evolution. Recently, the entanglement asymmetry has been introduced as a probe to study the interplay between symmetry breaking and relaxation in an extended quantum system. In particular, using the entanglement asymmetry, it has been shown that the more a symmetry is initially broken, the faster it may be restored. This surprising effect, which has also been observed in trapped-ion experiments, can be seen as a quantum version of the Mpemba effect, and is manifested by the crossing at a finite time of the entanglement asymmetry curves of two different initial symmetry-breaking configurations. In this paper we show that, by tuning the initial state, the symmetry dynamics in free fermionic systems can display much richer behavior than seen previously. In particular, for certain classes of initial states, including the ground states of free fermionic models with long-range couplings, the entanglement asymmetry can exhibit multiple crossings. This illustrates that the existence of the quantum Mpemba effect can only be inferred by examining the late-time behavior of the entanglement asymmetry.

Balancing bytes and bonds: Case studies in systemic approaches to digital dynamics in diverse family systems

Balancing bytes and bonds: Case studies in systemic approaches to digital dynamics in diverse family systems

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

The labor market channel of systemic risk

In this paper, we explore the labor market channel of systemic risk. We consider that distressed firms, besides defaulting on part of their debt commitments, also react to negative shocks by laying off part of their employees. This constitutes another source of systemic risk, as we assume that laid-off employees will default on their debt commitments. Using Brazilian data, we compute the systemic risk considering three possible strategies distressed firms can adopt: layoff of employees, default on debt commitments, or both strategies. Our findings highlight the importance of the labor market channel. It has contributed noticeably more to overall systemic risk during the study's assessment period (2015–2020). Moreover, the shock multiplier of the employees' layer is higher than that of the firms' layer. We also assess, through machine learning techniques, the determinants of firms' systemic impact under different layer configurations–that is, when the bank-firm layer is composed of the corporate loans extended to the firms, and when this is composed of the loans extended to the firms' employees. This study emphasizes the crucial role that the labor market plays in determining systemic risk dynamics and calls for improved risk management practices to address these issues effectively.

Prediction and Evaluation of Industrial Land Intensive Use: Adding Policy Impact to System Dynamics Model

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.

Notes from the field: Practical considerations for agent‐based and continuous representations of system dynamics

Notes from the field: Practical considerations for agent‐based and continuous representations of system dynamics

Circularity assessment of product-service systems using system dynamics modeling

Product-Service Systems (PSS) show the potential to implement the principles of a circular economy based on their characteristics, such as the use of an extended value network or the access of the PSS provider after distribution. PSS research shows that PSS are not inherently circular but must be designed specifically towards circularity. This requires an assessment method that can quantify the dimensions of circularity.In this paper, an assessment method based on system dynamics (SD) modeling is presented that allows the use of raw materials and the generation of waste to be evaluated in the context of PSS. The SD model is based on a conceptual integration of circular economy strategies into the lifecycle of PSS. The model formulation is described in detail and validated with several tests. The capabilities of the assessment approach are presented in an application scenario. The results show that SD modeling can be used for circularity assessment and can possibly serve as a starting point to enhance circularity.

Application of Reservoir Computers for Chaos Synchronization and Cracking Chaos-Based Cryptography in Fermionic 2D Thirring and 4D Gursey Models with Spinor Fields

Reservoir computers have recently become one of the most widely exploited model-free approaches to chaos synchronization due to their fast convergence speed and simple yet effective learning rules. In this study, reservoir computing has been utilized to emulate the dynamics of chaotic Thirring and Gursey systems. In the supervised learning of the reservoir, one-step ahead states of the dynamical systems have been employed as the teaching signals. After the training phase, the reservoir was first run autonomously and then weakly driven by chaotic systems. It has been shown that the trained reservoir computers can exhibit the same characteristics as the attractors of the learned chaotic systems, which enable their use in synchronization tasks of chaotic systems with possible application in cracking chaos-based cryptography. To investigate the effect of noise on the performance of reservoir computers, noise signals with different colors and amplitudes have been included in the transmitted signals. The obtained results indicate that the proposed scheme can provide lower error metrics for both models.