Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

A dynamic management model for sustainable drug supply chain in hospital pharmacies in Iran.

BACKGROUND: Sustainable supply chain management encompasses the strategic coordination and control of material, information, and financial flows, as well as the collaborative efforts among the entities engaged in the medicinal supply chain. This research proposes a dynamic and sustainable supply chain management model tailored explicitly for the inpatient pharmacies of Medical Centers and Hospitals affiliated with Iran University of Medical Sciences. METHODS: This is a quantitative study in terms of research objective and a qualitative study based on the stages in the conceptual development of the model. Therefore, the current study can be considered a mixed-methods approach. After identifying the key factors influencing the sustainability of the medicine supply chain, we conducted a dynamic analysis of the problem using system dynamics methodology. In order to simulate the system's behavior over 24 months, we utilized a combination of existing documentary information and expert opinions. The developed model was implemented using Vensim PLE software, allowing us to simulate and analyze the impact of various policies on the system. RESULTS: Medicine disposal exhibited an upward trend, particularly during the second 12-month period. Conversely, the trend of medicine expirations remained relatively stable in the initial months but showed an upward trajectory after that. The cost associated with disposed medicine experienced a consistent increase, with a higher rate observed during the second 12-month period. In contrast, sales of low-consumable medicine experienced a significant initial surge followed by a slower growth rate. Finally, the pharmacy's profit demonstrated an overall increasing trend, although the rate of increase was higher during the first 12 months. CONCLUSION: Among the various scenarios considered, namely "increasing the adequacy of human resources," "increasing the speed of response," and "utilizing pharmacists in the drug prescribing team," it was found that these interventions had a substantial effect on both enhancing the pharmacy's profit and reducing medication waste. Therefore, these scenarios were identified as having the most significant impact. The proposed model can serve as a valuable tool for forecasting and informing policy-making, providing insights into addressing the challenges associated with the sustainable drug supply chain in hospital pharmacies.

Projecting the Likely Impact of COVID-19 Infections on the Prevalence of Dementia in the United States.

Background: The association between COVID-19 infection and the onset of dementia among adults 65 years and older has the potential to increase the burden of dementia worldwide significantly. Our research, which focuses on understanding the likely increase in the burden of dementia due to COVID-19 infection in the USA, has crucial public policy implications. By providing these insights, we aim to empower policymakers, healthcare professionals, researchers, and public health officials to make informed decisions and plan for the future. Objective: Project the prevalence of dementia in the United States while accounting for the impact of COVID-19 infection on the onset of dementia. Methods: A dynamic multi-state population model was developed. The model was initialized with USA demographic data and estimates of age, gender, and race-specific transition rates from the Health and Retirement Study (HRS). Results: The projected increase in the burden of dementia among Americans 65 years and older is a staggering 14.838 million by 2050. However, due to the COVID-19 pandemic, we anticipate an additional 265,000 to 677,000 older adults 65 years and older will be affected by dementia. This will escalate the burden of dementia to a potential 15.103 million to 15.515 million by 2050, a significant human toll that we must be prepared for. Conclusions: The projected dementia numbers underscore the urgent need for policy and intervention in social care services and healthcare needs planning. This includes providing robust support systems for caregivers and ensuring the healthcare staff is adequately trained to meet the healthcare needs of dementia patients and their families.

Adapting Group Model Building for Mental Healthcare: A Participatory Co-Design Approach.

Countries around the world are experiencing both a growing need for mental health services and major gaps in the delivery of mental healthcare. The World Health Organisation's 2022 report on mental health emphasised co-designed and systems-level innovations responsive to the voice of those with lived experience. However, evidence for implementing such methodologies is sparse. Group model building (GMB), a participatory systems-based approach, has been used in various fields, but its application is limited within mental healthcare settings. It is recognised as a valuable tool for engaging stakeholders, including those with lived experience, in understanding and redesigning health systems and services. The participatory approach aligns with the mental health sector's shift towards co-design and person-centred care. This study assessed the acceptability of GMB as a method for co-design within mental healthcare, aiming to explore its potential for meaningful collaborative improvement and systems-level insights. Participants from a regional Mental Health and Drug and Alcohol Service (MHDAS) engaged in GMB workshops and focus groups. Eighteen individuals, including lived experience workers and healthcare professionals, provided feedback that informed adjustments to the workshops' design, language, timing and facilitation techniques, enhancing GMB's relevance for mental health co-design. The findings demonstrate the value and acceptability of the adapted GMB process in acute mental healthcare, marking a significant step towards evidence-based, person-centred service improvement. This study highlights the potential effectiveness of participatory methods in identifying and addressing systemic challenges, paving the way for future mental health reforms. Future work will focus on implementing and evaluating co-designed interventions, aiming for more inclusive and effective mental healthcare and advancing innovation and reform in the field.

Research on the recycling decision of WEEE in rural China under dual supervision.

Against the backdrop of promoting green, low-carbon, and high-quality development, this paper aims to promote efficient recycling of waste electrical and electronic equipment (WEEE) in rural China. In this paper, considering the integrated development of urban and rural areas and the standardization of industrial recycling system, under the joint action of the extension of producer responsibility and "dual regulation" of supply and marketing cooperatives, the evolutionary game model and system dynamics model of three-level recycling network of farmers, supply and marketing cooperatives and retailers are established. The mechanism of the participants to promote the recycling of WEEE in rural areas is discussed and the strategic choices and interactive relationships of various entities in the evolutionary process were used to analyze through the evolutionary game method. Meanwhile, using the theory of system dynamics, the main influencing factors of different evolution stages and the dynamic change process of the system are analyzed. The results show that: (1) supply and marketing cooperatives, retailers, and farmers can initially tend to participate in and supervise the recycling of WEEE; however, (2) they can finally achieve strong supervision, actively undertake and participate in the recycling and stabilization stable strategy of rural WEEE depends on their benefits and cost of expenditure expenditures are reasonable. (3) The strategic choice of supply and marketing cooperatives has the most significant impact on the strategic choice of retailers and the strategic choice of retailers has the most significant impact on the strategic choice of farmers.

Dynamic simulation of policy-driven green technology innovation networks: Digital empowerment and collaborative efficiency.

To delve into the intricate dynamics of green innovation, it is imperative to establish a policy-driven green innovation network and optimize its multi-entity collaborative mechanism. Given the dynamic complexity of a technological innovation network composed of multiple entities, this paper examines the interactions among four subsystems based on system dynamics (SD) simulation: resource input, innovation performance, policy-driven, and digital empowerment subsystem. Furthermore, we analyze the combined effects of policy-driven initiatives and the role of digital platforms in facilitating innovation efficiency based on empirical evidence. The results indicate that: (1) Government can effectively promote green development by enforcing stronger environmental regulations, such as increasing carbon trading price, while enhancing the emission reduction efficiency of innovative products. (2) Increased per capita R&D investment, along with financial, tax, fiscal incentives for innovation investment, will increase the rate of innovation achievements. (3) Government should strengthen talent policy during anticipated increases in talent numbers and reduce the intensity of introductions during expected declines. (4) By implementing incentive policies to develop S&T platforms, government can broaden innovation network cooperation, promotes resource aggregation, and leverages multi-entity cohort effects.

Dynamic impact analysis of the time-delay levitation control system on maglev vehicle system after adding smith predictor.

The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms.

A Holistic Way to Understand the Determinants of Physical Activity in Urban New South Wales, Australia: A Codesigned Systems Mapping Project.

BACKGROUND: To meet the World Health Organization goal of reducing physical inactivity by 15% by 2030, a multisectoral system approach is urgently needed to promote physical activity (PA). We report the process of and findings from a codesigned systems mapping project to present determinants of PA in the context of urban New South Wales, Australia. METHODS: A participatory conceptual mapping workshop was held in May 2023 with 19 participants working in education, transportation, urban planning, community, health, and sport and recreation. Initial maps were developed and refined using online feedback from the participants. Interviews were conducted with 10 additional policymakers from relevant sectors to further refine the maps. RESULTS: Two systems maps were cocreated, identifying over 100 variables influencing PA and their interconnections. Five settings emerged from the adults' map-social and community, policy, built environment and transportation, health care, and workplace-and 4 for the young people's map-family, school, transportation, and community and environment. The maps share similarities, such as regarding potential drivers within the transportation, community, and built environment sectors; however, the young people's map has a specific focus on the school setting and the adults' map on workplace and health care settings. Interviews with policymakers provided further unique insights into understanding and intervening in the PA system. CONCLUSIONS: This codesigned participatory systems mapping process, supplemented by stakeholder interviews, provided a unique opportunity to bring together stakeholders across sectors to understand the complexity within the PA system and begin to identify leverage points for tackling physical inactivity in New South Wales.

Critical Assessment of Information Back-Flow in Measurement-Free Teleportation.

We assess a scheme for measurement-free quantum teleportation from the perspective of the resources underpinning its performance. In particular, we focus on claims recently made about the crucial role played by the degree of non-Markovianity of the dynamics of the information carrier whose state we aim to teleport. We prove that any link between the efficiency of teleportation and the back-flow of information depends fundamentally on the way the various operations entailed by the measurement-free teleportation protocol are implemented while-in general-no claim of causal link can be made. Our result reinforces the need for the explicit assessment of the underlying physical platform when assessing the performance and resources for a given quantum protocol and the need for a rigorous quantum resource theory of non-Markovianity.

Designing a model to estimate the burden of COVID-19 in Iran.

The novel coronavirus disease 2019 (COVID-19) is the latest evidence of an epidemic disease resulting in an extraordinary number of infections and claimed several lives, along with extensive economic and social consequences. In response to the emergency situation, countries introduced different policies to address the situation, with different levels of efficacy. This paper outlines the protocol for developing a model to analyze the burden of COVID-19 in Iran and the effect of policies on the incidence and cumulative death of the disease. The importance of the model lies in the fact that no study, according to the authors' best knowledge, tried to quantify the impact of the disease on Iran society and the impact of various implemented interventions on disease control. Based on a systematic review of COVID-19 prediction models and expert interviews, we developed a system dynamics model that not only includes an epidemic part but also considers the impact of various policies implemented by the Ministry of Health. The epidemic model estimates the incidence and mortality of COVID-19 in Iran. The model also intends to evaluate the effect of implemented policies on these outcomes. The model reflects the continuum of COVID-19 infection and care in Iran (of which some of its elements are unique) and key activities and decisions in delivering care. The model is calibrated and validated using data published by the Ministry of Health of Iran. Finally, the study aims to provide evidence of the impact of interventions intended to curb COVID-19 in Iran. Insights provided by the model will be necessary for controlling either future waves of the disease or similar future pandemics.

Reducing mental health emergency visits: population-level strategies from participatory modelling.

BACKGROUND: Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011-2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations. METHODS: We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023-2032 for youth (aged 15-24) and for the general population. RESULTS: Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032. CONCLUSIONS: Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms.

Modeling learning-oriented motivation in health students: a system dynamics approach.

BACKGROUND: Evidence shows that motivational practices focused on utility, importance, and autonomy shape university students' motivational orientation toward learning. On the other hand, the relationship between these variables and motivational orientation toward learning is not linear and requires models that describe their behavior over time. METHOD: In this study, mathematical modeling based on system dynamics methodology is used to simulate in health students the temporal dynamics of the motivational orientation toward learning based on the behavior of these variables in different scenarios. RESULTS: The results indicate that a) Mastery is sensitive to changes in frequency when importance and autonomy practices are performed; b) the development of Mastery is critical in the first three semesters of academic life, but its loss is hardly recoverable even when practices are incorporated in subsequent semesters; c) Utility-focused motivational practices have no significant effect on the development of learning-oriented motivation. CONCLUSION: These findings have significant practical implications for higher education. Understanding the critical role of Mastery in the early stages of academic life and the limited potential for recovery if lost can help raise awareness of the importance of early implementation of motivational practices focused on relevance and autonomy.

Application of MIDD to accelerate the development of anti-infectives: Current status and future perspectives.

This review examines the role of model-informed drug development (MIDD) in advancing antibacterial and antiviral drug development, with an emphasis on the inclusion of host system dynamics into modeling efforts. Amidst the growing challenges of multidrug resistance and diminishing market returns, innovative methodologies are crucial for continuous drug discovery and development. The MIDD approach, with its robust capacity to integrate diverse data types, offers a promising solution. In particular, the utilization of appropriate modeling and simulation techniques for better characterization and early assessment of drug resistance are discussed. The evolution of MIDD practices across different infectious disease fields is also summarized, and compared to advancements achieved in oncology. Moving forward, the application of MIDD should expand into host system dynamics as these considerations are critical for the development of "live drugs" (e.g. chimeric antigen receptor T cells or bacteriophages) to address issues like antibiotic resistance or latent viral infections.

Multiple benefits of new-energy vehicle power battery recycling strategies based on the theory of planned behavior and stimulus organism response.

With the yearly increasing market penetration of new-energy vehicles in China, the retirement of power batteries has gradually become a scale, and most of the waste batteries have entered informal recycling channels, which has induced a series of environmental problems. Considering this issue, we introduced the system dynamics (SD), stimulus organism response (SOR), and the theory of planned behavior (TPB) in behavioral economics to establish the environmental economic benefit evaluation model of power battery recycling strategies, and we performed a dynamic simulation analysis on the effect of government subsidy policy, policy advocacy, and other recycling strategies. The results show that: (1) the recovery subsidy policy can improve the formal recycling quantity and economic benefits of recovery, but the effect on the degree of environmental pollution is limited. (2) The combination of environmental awareness promotion strategy and subsidy policy can overcome the shortcomings of subsidy policy and has significant environmental and economic performance. (3) Compared with the benchmark scenario, the formal recycling quantity, the CO2 emission reduction, and the economic benefits of recovery in scenario 4 (high subsidy-high policy propaganda strategy) increased by approximately 112 %, 208 %, and 223 %, respectively, and the degree of environmental pollution decreased by approximately 65 %.

Qualitative system dynamics modelling to support the design and implementation of tuberculosis infection prevention and control measures in South African primary health care facilities.

Tuberculosis infection prevention and control (TB IPC) measures are a cornerstone of policy, but measures are diverse and variably implemented. Limited attention has been paid to the health system environment which influences successful implementation of these measures. We used qualitative system dynamics and group-model-building methods 1) develop a qualitative causal map of the interlinked drivers of Mycobacterium tuberculosis (Mtb) transmission in South African primary health care facilities which in turn, helped us to 2) identify plausible IPC interventions to reduce risk of transmission. Two one-day participatory workshops were held in 2019 with policy- and decision-makers at national and provincial level, and patient advocates and health professionals at clinic and district level. Causal loop diagrams were generated by participants and combined by investigators. The research team reviewed diagrams to identify the drivers of nosocomial transmission of Mtb in primary health care facilities. Interventions proposed by participants were mapped onto diagrams to identify anticipated mechanisms of action and effect. Three systemic drivers were identified: 1) Mtb nosocomial transmission is driven by bottlenecks in patient flow at given times; 2) IPC implementation and clinic processes are anchored within a staff "culture of nominal compliance"; and 3) limited systems-learning at policy level inhibits effective clinic management and IPC implementation. Interventions prioritised by workshop participants included infrastructural, organisational, and behavioural strategies that target three areas: 1) improve air quality; 2) improve use of personal protective equipment; and 3) reduce the number of individuals in the clinic. In addition to core mechanisms, participants elaborated specific additional enablers that would help sustain implementation. Qualitative system dynamics modelling (SDM) methods allowed us to capture stakeholder views and potential solutions to address the problem of sub-optimal TB IPC implementation. The participatory elements of SDM facilitated problem-solving and inclusion of multiple factors frequently neglected when considering implementation.

ACL Injury Etiology in Its Context: A Systems Thinking, Group Model Building Approach.

Background/Objectives: Given the complex nature of Anterior Cruciate Ligament (ACL) injury, it is important to analyze its etiology with suitable approaches in order to formulate intervention strategies for effective prevention. The present study employs system thinking techniques to develop a Causal Loop Diagram (CLD) Model for investigating the risk factors for ACL Injury (CLD-ACLI), through a Group Model Building approach. Methods: A two-stage procedure was applied involving a comprehensive literature review followed by several systems thinking group-modeling co-creation workshops with stakeholders. Results: Based on input from experts and stakeholders, combined with the latest scientific findings, the derived CLD-ACLI model revealed a series of interesting complex nonlinear interrelationships causal loops between the likelihood of ACL injury and the number of risk factors. Particularly, the interaction among institutional, psychological, neurocognitive, neuromuscular, malalignment factors, and trauma history seem to affect neuromuscular control, which subsequently may alter the biomechanics of landing, predisposing the ACL to injury. Further, according to the proposed CLD-ACLI model, the risk for injury may increase further if specific environmental and anatomical factors affect the shear forces imposed on the ACL. Conclusions: The proposed CLD-ACLI model constitutes a rigorous useful conceptual presentation agreed upon among experts on the dynamic interactions among potential intrinsic and extrinsic risk factors for ACL injury. The presented causal loop model constitutes a vital step for developing a validated quantitative system dynamics simulation model for evaluating ACL injury-prevention strategies prior to implementation.

Neuronal network dynamics in the posterodorsal amygdala: shaping reproductive hormone pulsatility.

Normal reproductive function and fertility rely on the rhythmic secretion of gonadotropin-releasing hormone (GnRH), which is driven by the hypothalamic GnRH pulse generator. A key regulator of the GnRH pulse generator is the posterodorsal subnucleus of the medial amygdala (MePD), a brain region that is involved in processing external environmental cues, including the effect of stress. However, the neuronal pathways enabling the dynamic, stress-triggered modulation of GnRH secretion remain largely unknown. Here, we employ in silico modelling in order to explore the impact of dynamic inputs on GnRH pulse generator activity. We introduce and analyse a mathematical model representing MePD neuronal circuits composed of GABAergic and glutamatergic neuronal populations, integrating it with our GnRH pulse generator model. Our analysis dissects the influence of excitatory and inhibitory MePD projections' outputs on the GnRH pulse generator's activity and reveals a functionally relevant MePD glutamatergic projection to the GnRH pulse generator, which we probe with in vivo optogenetics. Our study sheds light on how MePD neuronal dynamics affect the GnRH pulse generator activity and offers insights into stress-related dysregulation.

Dynamic marginal cost curves to support water resources management.

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

Seeking a pathway towards a more sustainable human-water relationship by coupled model - From a perspective of socio-hydrology.

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010-2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Comprehensive evaluation of "Three Waters" carrying capacity and path evolution study: A case of the Yellow River Basin.

Research focusing solely on the carrying capacity of a single aspect of water resources, water environment, or water ecology is no longer sufficient to support the sustainable development and management of basin water systems. The study of basin carrying capacity should expand towards a comprehensive and holistic direction. Therefore, this study constructed an evaluation index system for carrying capacity based on water resources, water environment, and water ecology ("Three Waters"). Utilizing the entropy weight-TOPSIS method, System Comprehensive Index Evaluation, and ArcGIS tools, the comprehensive evaluation index of the "Three Waters" System Carrying Capacity (TWSCC) in the Yellow River Basin (YRB) from 2005 to 2020 was calculated. The evaluation index analyzed the spatiotemporal variation characteristics of subsystem carrying capacity and performed early warning identification and analysis of TWSCC. Four differentiated developmental pathways were designed based on the current status of basin carrying capacity. Leveraging System Dynamics (SD) modeling, the dynamic simulation, and emulation of carrying capacity trends in the YRB from 2020 to 2035 were conducted. The research findings indicate that from 2005 to 2020, the TWSCC levels across the nine provinces in the YRB consistently exhibited varying degrees of overload. The alert levels mostly remained in "Heavy warning" or "Medium warning" states. By 2035, TWSCC under the four development paths improved from 2020 levels, with the Green Environmental Protection-Oriented scheme reaching a safe carrying capacity. In summary, this paper offers theoretical and methodological support for developing basin-carrying capacity and the integrated governance of "Three Waters."