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Testing for COVID-19 is a key intervention that supports tracking and isolation to prevent further infections. However, diagnostic tests are a scarce and finite resource, so abundance in one country can quickly lead to shortages in others, creating a competitive landscape. Countries experience peaks in infections at different times, meaning that the need for diagnostic tests also peaks at different moments. This phase lag implies opportunities for a more collaborative approach, although countries might also worry about the risks of future shortages if they help others by reallocating their excess inventory of diagnostic tests. This article features a simulation model that connects three subsystems: COVID-19 transmission, the diagnostic test supply chain, and public policy interventions aimed at flattening the infection curve. This integrated system approach clarifies that, for public policies, there is a time to be risk-averse and a time for risk-taking, reflecting the different phases of the pandemic (contagion vs. recovery) and the dominant dynamic behavior that occurs in these phases (reinforcing vs. balancing). In the contagion phase, policymakers cannot afford to reject extra diagnostic tests and should take what they can get, in line with a competitive mindset. In the recovery phase, policymakers can afford to give away excess inventory to other countries in need (one-sided collaboration). When a country switches between taking and giving, in a form of two-sided collaboration, it can flatten the curve, not only for itself but also for others.

Large‐scale power systems exhibit more complex dynamics due to the increasing integration of inverter‐based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide‐Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large‐scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)‐based advanced applications are introduced, including AI‐based inertia estimation, AI‐based disturbance size and location estimation, AI‐based system stability assessment, and AI‐based data authentication.

Neural network is one of the most widely used approximators for reinforcement learning (RL) methods and model‐learning approaches. This chapter discusses H 2 neural control based on recurrent neural networks and RL for robots, which has better performance than the standard linear quadratic regulator control and also accelerates the learning process. The controllers are designed in both discrete time and continuous time. Control of discrete‐time systems is becoming important in recent years since almost all of the control schemes are implemented on digital devices. The neural RL has good mean squared error results by taking into account measures of the position and velocity states and the modeling error. The chapter presents stability and convergence using Lyapunov stability theory and the contraction property. Simulations results show that the RL approach presents optimal and robust performances without knowledge of the system dynamics and large learning time.

Exchange rate volatility is a vital issue in international finance due to its effect on the value of the firm, depending on firm's exposure to the exchange rate risk. However, this impact is not clear in literature as evidence is mixed. This article analyses the impact of exchange rate fluctuations on firm value. A system dynamics-based corporate planning model has been developed that incorporates the modules for financial and physical processes, firm valuation and exchange rate determination. Simulation results reveal that a stronger home currency leads to an increase in market price per share of the firm and vice versa. The results provide counter intuitive evidence on the impact of exchange rate on firm value and provide nonlinear feedback loop-based explanation. The study fills the gap in literature by incorporating a systemic approach in the strategic planning process to assess the composite impact of parameter changes on key variables. publishedVersion

Utility of community-based system dynamics (CBSD) models on stigmatized public health issues and health disparities depends upon how representative the model is to real-world experience within the community. “Personal experience” participants (PEP), especially from marginalized groups, are essential model contributors, but are often underrepresented in modeling groups due to multiple barriers. This study details a method to increase PEP representation for models on stigmatized issues. We use a case study from a CBSD project on health disparities within the association between alcohol misuse (AM) and intimate partner violence (IPV) for Northern Plains Indigenous women. Short group model building sessions were held at three community organizations providing relevant resources. Each model contributed unique system components, and there were few similarities between models. A consolidated model provided a rich picture of the complex system. Adding brief PEP-based group modeling sessions can enhance PEP representation in model development for stigmatized public health issues.

In developing countries, the contradiction between environmental protection and economic development has become increasingly serious. The current research focuses on: how to coordinate the relationship between economic development and environmental protection. This study considers energy consumption and environmental emission reduction as the research object. An energy–economy–environment simulation system was developed using Vensim as a modeling platform to clarify the relationship between energy consumption, economic development, and environmental pollution. Based on the data gleaned from Beijing's energy system, the availability of the simulation model is verified through historical inspection and operational tests. Finally, based on the energy–economy–environment simulation system, different development scenarios have been set up to analyze the development trends of energy consumption, economic development, and environmental emission reduction under different scenarios. The results show that the development prospects under the coordinated development scenario are the most reasonable and effective, taking into account the coordinated development of all the three aspects, especially in terms of carbon emission reduction.

The bridge crane system is a typical under‐actuated system that is widely used in production and life. Although various scholars have conducted extensive research on the bridge crane system in recent years, there are still many problems, such as the trajectory planning of the cart and the anti‐sway control of the cargo. In order to tackle the problem of the anti‐sway control of the cargo, a hierarchical global fast terminal sliding‐mode control (H‐GFTSMC) is developed in this work. First, the Lagrange equations are used to model the system dynamics. Then, an appropriate hierarchical global fast terminal sliding‐mode controller is designed to achieve anti‐sway control of the cargo, and it is proved that each sliding‐mode surface is progressively stable. A series of simulations were implemented to verify the effectiveness of the control method. The simulation results show that the H‐GFTSMC has better control performance compared with the proportional–integral–derivative control method. When changing the cable length or adding non‐negligible noise to the system, the H‐GFTSMC still has good robustness.

This chapter discusses special attributes of the specific power electronic‐based technologies to enhance system dynamics. It also discusses optimal placement of the technologies in detail. After a discussion on the application of virtual synchronous generators (VSGs) in power system stability and performance improvement, optimal placement of dispatchable inertia besides the associated realization mechanisms would be emphasized. Without the loss of generality, while distributed generations (such as wind farms), high‐voltage direct current systems, and energy storage systems (ESSs) could emulate virtual inertia, the ESSs are considered as the source of virtual inertia in this chapter. Manipulation of the inertia constant by the VSGs could enhance system dynamics and stability. This could be realized using inertia‐based control schemes. Indeed, inertia provision plays an important role in designing advanced control schemes in modern power grids. Optimal placement of virtual inertia can be studied in two perspectives: frequency stability and small‐signal stability.

BACKGROUND Frameworks such as the WSCC model provide evidence‐based guidance for addressing school health at the school, district, and regional level. However, frameworks do not implement themselves; they require the mobilization and collaboration of stakeholders within communities and an understanding of the unique resources and barriers within each context. Furthermore, addressing school health presents a complex systems problem. METHODS Community‐based system dynamics (CBSD) is a participatory approach for engaging communities in understanding and changing complex systems. We used a descriptive multiple case study design to evaluate how and why CBSD was used as a tool for stakeholders to engage with the complexity of school health. RESULTS We analyzed 3 cases to understand how these methods were used to enhance collaboration, analysis, and community action at multiple levels, including in 2 school districts, with a city‐wide stakeholder committee, and with a group of high school students. CONCLUSIONS Community‐based system dynamics presents a promising approach for building shared language and ownership among stakeholders, tailoring to local community contexts, and mobilizing stakeholders for action based on new system insights. We close with a discussion of unique opportunities and challenges of expanding the use of CBSD in the field of school health.

In the 1972 bestseller Limits to Growth (LtG), the authors concluded that, if global society kept pursuing economic growth, it would experience a decline in food production, industrial output, and ultimately population, within this century. The LtG authors used a system dynamics model to study interactions between global variables, varying model assumptions to generate different scenarios. Previous empirical‐data comparisons since then by Turner showed closest alignment with a scenario that ended in collapse. This research constitutes a data update to LtG, by examining to what extent empirical data aligned with four LtG scenarios spanning a range of technological, resource, and societal assumptions. The research benefited from improved data availability since the previous updates and included a scenario and two variables that had not been part of previous comparisons. The two scenarios aligning most closely with observed data indicate a halt in welfare, food, and industrial production over the next decade or so, which puts into question the suitability of continuous economic growth as humanity's goal in the twenty‐first century. Both scenarios also indicate subsequent declines in these variables, but only one—where declines are caused by pollution—depicts a collapse. The scenario that aligned most closely in earlier comparisons was not amongst the two closest aligning scenarios in this research. The scenario with the smallest declines aligned least with empirical data; however, absolute differences were often not yet large. The four scenarios diverge significantly more after 2020, suggesting that the window to align with this last scenario is closing.

Chemie - Ingenieur - Technik : CIT (2020). doi:10.1002/cite.202000048, Published by Wiley-VCH Verl., Weinheim

MRI‐based neuroimaging techniques have been used to investigate brain injury associated with HIV‐infection. Whole‐brain cortical mean‐field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean‐field dynamic modeling to investigate structural and functional connectivity in 42 HIV‐infected subjects before and after 12‐week of combination antiretroviral therapy (cART) and compared them with 46 age‐matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region‐specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV‐infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement., In this study, we adopted the relaxed mean‐field dynamic modeling to investigate structural and functional connectivity in 42 HIV‐infected subjects before and after 12 week of combination antiretroviral therapy (cART) and compared them with 46 age‐matched healthy subjects. The results show that untreated HIV‐infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.

Based on the control measures at different stages of COVID-19 and its transmission characteristics, we propose a dynamical model to describe the transmission of this virus among wild animals, human population, and the environment, where the migration of humans, asymptomatic infected humans, and the self-protection awareness of susceptible humans are also introduced. We discuss, firstly, the existence and stability of the disease-free and endemic equilibrium of wild animals and environment-only model; this happens to be the initial stage of disease transmission. Further, with the intervention of control measures, the model degenerates into a subsystem with only human population and environment; the existence and stability of the disease-free equilibrium of this subsystem are studied. With the strengthening of control strategies, the environment and human population subsystems have further degenerated into a human population-only model with blocking migration and environment; the ultimate extinction of this disease and the peak of the outbreak are researched. Finally, some numerical simulations are carried to explain the main results and the impacts of various control strategies on the disease outbreak. In particular, we also fit the confirmed cases in Wuhan from 24 January to 5 March to illustrate the rationality and effectiveness of our model. The results show that, in the early stage of the outbreak of COVID-19, stopping all the migration of humans, increasing the intensity of treatment, enhancing the awareness of personal protection, and especially increasing the ability to identify asymptomatic infections are indispensable control means to control this disease.

Supply chains are composed of multiple stakeholders who have complex interrelationships. In addition, the forward and reverse flow of materials, information, human resources, and finance occurs among different stakeholders in closing the loop of supply chains. Reverse logistics (RL) activities are gaining importance in terms of size and quantity due to both economic and environmental concerns. These flows in RL in supply chains are both dynamic and complex in nature. Further, the environmental impact of RL activities has barely been considered in holistic way in available literature. In this study, a system dynamics model has been developed to analyze and comprehend the green performance of RL activities by predicting the environmental impact of RL activities. The proposed model has been validated by a case study in the context of a food supply chain. In the company where the case study is carried out, the environmental effects of RL activities have been analyzed. These activities in a food supply chain in terms of CO2 (carbon dioxide), NOx (nitrogen oxide), SO2 (sulfur dioxide), and PM (particulate matter) emissions have been predicted through a system dynamics model for the years 2020 to 2024. The proposed methodology is applied in a food supply context, a major player in retail business, especially in emerging economies. According to our findings, the RL activities in a food supply chain can significantly contribute to green performance management by minimizing food waste and loss; hence, the environmental impacts of such activities should be closely examined from a managerial perspective.

Model‐informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients’ survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.

Humanitarian disasters are highly dynamic and uncertain. The shifting situation, volatility of information, and the emergence of decision processes and coordination structures require humanitarian organizations to continuously adapt their operations. In this study, we aim to make headway in understanding adaptive decision-making in a dynamic interplay between changing situation, volatile information, and emerging coordination structures. Starting from theories of sensemaking, coordination, and decision-making, we present two case studies that represent the response to two different humanitarian disasters: Typhoon Haiyan in the Philippines, and the Syria Crisis, one of the most prominent ongoing conflicts. For both, we highlight how volatile information and the urge to respond via sensemaking lead to fragmentation and misalignment of emergent coordination structures and decisions, which, in turn, slow down adaptation. Based on the case studies, we derive propositions and the need to continuously align laterally between different regions and hierarchically between operational and strategic levels to avoid persistence of coordination-information bubbles. We discuss the implications of our findings for the development of methods and theory to ensure that humanitarian operations management captures the critical role of information as a driver of emergent coordination and adaptive decisions.

The complexity surrounding the maximization of firm value agenda demands a comprehensive causal model that effectively embeds the intertwining relationships of the variables and the policies involved. System dynamics provides an appropriate methodology to model and simulate such complex relationships to facilitate decision making in a complex business environment. The objective of the study is to analyze the impact of capital structure policy, being a key managerial decision, on the firm value. For this purpose, the study develops a system dynamics‐based corporate planning model for an oil firm, including the operational as well as financial processes. Various scenarios and capital structure policies have been designed and simulated to identify the policy that helps in increasing the firm value. The results demonstrate that increase in debt percentage in capital structure mix increase the firm value. publishedVersion

Research on government formation in parliamentary democracies has presented contradicting evidence on the role of political veto institutions and parliamentary polarization on the formation of cabi ...