System Dynamics Research Articles

Complexities of implementing Maternal and Perinatal Death Surveillance and Response in crisis-affected contexts: a comparative case study

BackgroundMaternal and Perinatal Death Surveillance and Response (MPDSR) systems provide an opportunity for health systems to understand the determinants of maternal and perinatal deaths in order to improve quality of care and prevent future deaths from occurring. While there has been broad uptake and learning from low- and middle-income countries, little is known on how to effectively implement MPDSR within humanitarian contexts – where disruptions in health service delivery are common, infrastructural damage and insecurity impact the accessibility of care, and severe financial and human resource shortages limit the quality and capacity to provide services to the most vulnerable. This study aimed to understand how contextual factors influence facility-based MPDSR interventions within five humanitarian contexts.MethodsDescriptive case studies were conducted on the implementation of MPDSR in Cox’s Bazar refugee camps in Bangladesh, refugee settlements in Uganda, South Sudan, Palestine, and Yemen. Desk reviews of case-specific MPDSR documentation and in-depth key informant interviews with 76 stakeholders supporting or directly implementing mortality surveillance interventions were conducted between December 2021 and July 2022. Interviews were recorded, transcribed, and analyzed using Dedoose software. Thematic content analysis was employed to understand the adoption, penetration, sustainability, and fidelity of MPDSR interventions and to facilitate cross-case synthesis of implementation complexities.ResultsImplementation of MPDSR interventions in the five humanitarian settings varied in scope, scale, and approach. Adoption of the interventions and fidelity to established protocols were influenced by availability of financial and human resources, the implementation climate (leadership engagement, health administration and provider buy-in, and community involvement), and complex humanitarian-health system dynamics. Blame culture was pervasive in all contexts, with health providers often facing punishment or criminalization for negligence, threats, and violence. Across contexts, successful implementation was driven by integrating MPDSR within quality improvement efforts, improving community involvement, and adapting programming fit-for-context.ConclusionsThe unique contextual considerations of humanitarian settings call for a customized approach to implementing MPDSR that best serves the immediate needs of the crisis, aligns with stakeholder priorities, and supports health workers and humanitarian responders in providing care to the most vulnerable populations.

Inferring potential landscapes: A Schrödinger bridge approach to maximum caliber

Schrödinger bridges have emerged as an enabling framework for unveiling the stochastic dynamics of systems based on marginal observations at different points in time. The terminology “bridge” refers to a probability law that suitably interpolates such marginals. The theory plays a pivotal role in a variety of contemporary developments in machine learning, stochastic control, thermodynamics, and biology, to name a few, impacting disciplines such as single-cell genomics, meteorology, and robotics. In this work, we extend Schrödinger's paradigm of bridges to account for integral constraints along paths, in a way akin to maximum caliber—a maximum entropy principle applied in a dynamic context. The maximum caliber principle has proven useful to infer the dynamics of complex systems, e.g., model gene circuits and protein folding. We unify these two problems via a maximum likelihood formulation to reconcile stochastic dynamics with ensemble-path data. A variety of data types can be encompassed, ranging from distribution moments to average currents along paths. The framework enables inference of time-varying potential landscapes that drive the process. The resulting forces can be interpreted as the optimal control that drives the system in a way that abides by specified integral constraints. This, in turn, relates to a similarly constrained optimal mass transport problem in the zero-noise limit. Analogous results are presented in a discrete-time, discrete-space setting and specialized to steady-state dynamics. We finish by illustrating the practical applicability of the framework through paradigmatic examples, such as that of bit erasure or protein folding. In doing so, we highlight the strengths of the proposed framework, namely, the generality of the theory, its elegant analytical structure, the ease of computation, and the ability to interpret results in terms of system dynamics. This is in contrast to maximum caliber problems where the focus is typically on updating a probability law on paths. Published by the American Physical Society 2024

Concave Gold Nanocubes Exhibit Growth-Etching Behavior: Unexpected Morphological Transformations.

Chemical equilibrium stands as a fundamental principle governing the dynamics of chemical systems. However, it may become intricate when it refers to nanomaterials because of their unique properties. Here, we invesitigated concave gold nanocubes (CGNs) subjected to an akaline Au3+/H2O2 solution, which exhibit both etching and growth in a monotonic solution. When CGNs were subjected to an increasingly alkaline Au3+/H2O2 solution, their dimensions increased from 107 to 199 nm and then decreased to 125 nm. Transmission electron microscopy (TEM) demonstrated that their morphology undergoes intricate alternations from concave to mutibranch and finally to concave again. Real-time ultraviolet-visible spectroscopy and time-dependent TEM also demonstrated reduction first and then oxidation in one solution. Among the nanomaterials, the obtained carpenterworm-like gold nanoparticles revealed the best catalytic performance in p-nitrophenol reduction by NaBH4, with a chemical rate that continues to increase until the reaction reaches completion. Growth leading to atomic dislocation, distortion, and exposure on nanoparticles and the redox of H2O2 plausibly account for the further etching due to the Ostwald ripening effect. Our study may spur more interest in the tuning of the properties, engineering, investigation, and design of new kinds of nanomaterials.

Dynamics of the epidemiological Predator-Prey system in advective environments.

This paper aims to establish the existence of traveling wave solutions connecting different equilibria for a spatial eco-epidemiological predator-prey system in advective environments. After applying the traveling wave coordinates, these solutions correspond to heteroclinic orbits in phase space. We investigate the existence of the traveling wave solution connecting from a boundary equilibrium to a co-existence equilibrium by using a shooting method. Different from the techniques introduced by Huang, we directly prove the convergence of the solution to a co-existence equilibrium by constructing a special bounded set. Furthermore, the Lyapunov-type function we constructed does not need the condition of bounded below. Our approach provides a different way to study the existence of traveling wave solutions about the co-existence equilibrium. The existence of traveling wave solutions between co-existence equilibria are proved by utilizing the qualitative theory and the geometric singular perturbation theory. Some other open questions of interest are also discussed in the paper.

Quantum fluctuation dynamics of open quantum systems with collective operator-valued rates, and applications to Hopfield-like networks

Abstract We consider a class of open quantum many-body systems that evolves in a Markovian fashion, the dynamical generator being in GKS-Lindblad form. Here, the Hamiltonian contribution is characterized by an all-to-all coupling, and the dissipation features local transitions that depend on collective, operator-valued rates, encoding average properties of the system. These types of generators can be formally obtained by generalizing, to the quantum realm, classical (mean-field) stochastic Markov dynamics, with state-dependent transitions. Focusing on the dynamics emerging in the limit of infinitely large systems, we build on the exactness of the mean-field equations for the dynamics of average operators. In this framework, we derive the dynamics of quantum fluctuation operators, that can be used in turn to understand the fate of quantum correlations in the system. We then apply our results to quantum generalized Hopfield associative memories. Here we show that, asymptotically and at the description level of quantum fluctuations, only a very weak amount of quantum correlations, in the form of quantum discord, emerges beyond classical correlations.

Towards Robust Data-Driven Automated Recovery of Symbolic Conservation Laws from Limited Data

Abstract Conservation laws are an inherent feature in many systems modeling real world phenomena, in particular, those modeling biological and chemical systems. If the form of the underlying dynamical system is known, linear algebra and algebraic geometry methods can be used to identify the conservation laws. Our work focuses on using data-driven methods to identify the conservation law(s) in the absence of the knowledge of system dynamics. We develop a robust data-driven computational framework that automates the process of identifying the number and type of the conservation law(s) while keeping the amount of required data to a minimum. We demonstrate that due to relative stability of singular vectors to noise we are able to reconstruct correct conservation laws without the need for excessive parameter tuning. While we focus primarily on biological examples, the framework proposed herein is suitable for a variety of data science applications and can be coupled with other machine learning approaches.

Early Predictor for the Onset of Critical Transitions in Networked Dynamical Systems

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa. Published by the American Physical Society 2024

Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation.

Conventional molecular dynamics (MD) simulations struggle when simulating particles with steeply varying interaction potentials due to the need to use a very short time step. Here, we demonstrate that an event-driven Monte Carlo (EDMC) approach was first introduced by Peters and de With [Phys. Rev. E 85, 026703 (2012)] and represents an excellent substitute for MD in the canonical ensemble. In addition to correctly reproducing the static thermodynamic properties of the system, the EDMC method closely mimics the dynamics of systems of particles interacting via the steeply repulsive Weeks-Chandler-Andersen (WCA) potential. In comparison to time-driven MD simulations, EDMC runs faster by over an order of magnitude at sufficiently low temperatures. Moreover, the lack of a finite time step in EDMC circumvents the need to trade accuracy against the simulation speed associated with the choice of time step in MD. We showcase the usefulness of this model to explore the phase behavior of the WCA model at extremely low temperatures and to demonstrate that spontaneous nucleation and growth of the Laves phases are possible at temperatures significantly lower than previously reported.

Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China

Amidst the challenges posed by global climate change and accelerated urbanization, the structure and distribution of land use are shifting dramatically, exacerbating ecological and land-use conflicts, particularly in China. Effective land resource management requires accurate forecasts of land use and cover change (LUCC). However, the future trajectory of LUCC, influenced by climate change and urbanization, remains uncertain. This study developed an integrated multi-scenario framework by combining system dynamics and patch-generating land use simulation models to predict future LUCC in high-density urban regions under various Shared Socioeconomic Pathway (SSP)–Representative Concentration Pathway (RCP) scenarios. The results showed the following: (1) From 2020 to 2050, cultivated land, unused land, and water are projected to decrease, while construction land is expected to increase. (2) Future land use patterns exhibit significant spatial heterogeneity across three scenarios. Construction land will expand in all districts of Hangzhou, particularly in the main urban areas. Under the SSP585 scenario, the expansion of construction land is most significant, while it is the least under the SSP126 scenario. (3) Distinct factors drive the expansion of different land use types. The digital elevation model is the predominant factor for the expansion of forest and grassland, contributing 19.25% and 30.76%, respectively. Night light contributes the most to cultivated land and construction land, at 13.94% and 20.35%, respectively. (4) The average land use intensity (LUI) in central urban districts markedly surpasses that in the surrounding suburban areas, with Xiacheng having the highest LUI and Chun’an the lowest. Under the SSP126 scenario, the area with increased LUI is significantly smaller than under the SSP245 and SSP585 scenarios. These findings offer valuable guidance for sustainable planning and built environment management in Hangzhou and similarly situated urban centers worldwide.

Robust error-state Kalman-type filters for attitude estimation

State estimation techniques appear in a plethora of engineering fields, in particular for the attitude estimation application of interest in this contribution. A number of filters have been devised for this problem, in particular Kalman-type ones, but in their standard form they are known to be fragile against outliers. In this work, we focus on error-state filters, designed for states living on a manifold, here unit-norm quaternions. We propose extensions based on robust statistics, leading to two robust M-type filters able to tackle outliers either in the measurements, in the system dynamics or in both cases. The performance and robustness of these filters is explored in a numerical experiment. We first assess the outlier ratio that they manage to mitigate, and second the type of dynamics outliers that they can detect, showing that the filter performance depends on the measurements’ properties.

Dynamics of memristive liu system and its DSP implementation.

Dynamics of memristive liu system and its DSP implementation.

On the Dynamics of a Three-dimensional Differential System Related to the Normalized Ricci Flow on Generalized Wallach Spaces

On the Dynamics of a Three-dimensional Differential System Related to the Normalized Ricci Flow on Generalized Wallach Spaces

Different patterns of coupled predator-prey dynamics when the same species interact in different locations.

Individual predator and prey species exhibit coupled population dynamics in simple laboratory systems and simple natural communities. It is unclear how often such pairwise coupling occurs in more complex communities, in which an individual predator species might feed on several prey species and an individual prey species might be attacked by several predators. To examine this problem, we applied multivariate autoregressive state-space (MARSS) models to 5-year time-series of monthly surveys of a predatory fish, the eastern mosquitofish (Gambusia holbrooki), and its littoral zone prey species, the least killifish (Heterandria formosa), in three locations in north Florida. The MARSS models were consistent with coupled predator-prey dynamics at two of the three locations. In one of these two locations, the estimated densities of the two species displayed classic predator-prey oscillations. In the third location, there was a positive effect of killifish density on mosquitofish density but no detectable effect of mosquitofish density on killifish density. In all three locations, increased submergent vegetation cover was associated with increased prey density but not increased predator density. Eigenvalues analyses for the joint predator-prey dynamics indicated that one of the cyclic locations had more stable dynamics than the other locations. The three different patterns demonstrate that the dynamics of a pairwise predator-prey interaction emerge not only from the characteristics of the prey and the predator, but also those of the habitat and trophic web in which the predator and prey are embedded.

Proposing a Novel System for Measuring the Effectiveness of Validating Customers of the Banking System based on the System Dynamics

Proposing a Novel System for Measuring the Effectiveness of Validating Customers of the Banking System based on the System Dynamics

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Abstract Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.

Exploring cooperative hunting dynamics and PRCC analysis: insights from a spatio-temporal mathematical model

The proposed mathematical model explores the intricate dynamics of a predator-prey system involving prey infection and cooperative hunting of predators. The model incorporates habitat complexity, emphasizing its influence on ecological interactions. The well-posedness of the system has rigorously been examined in a temporal setting and also conducted stability analysis. The bifurcation analysis reveals the existence of several local bifurcations on the system, namely transcritical bifurcation, saddle-node bifurcation, and Hopf bifurcation. Furthermore, these investigations delineate the two-dimensional bifurcations including Bogdanov–Takens and cusp bifurcations for different parametric combinations. With suitable choices of parameter values, the proposed model exhibits diverse dynamic phenomena, including bistable and tri-stable behavior. Latin hypercube sampling is utilized to conduct uncertainty analysis on input parameters, aiming to observe their effects on population dynamics. Subsequently, Kendall’s tau and Spearman’s rank correlation coefficients are also computed to investigate the impact of these uncertainties on the population. In the later part, a spatio-temporal system is proposed with two-dimensional diffusion terms to obtain the conditions for Turing instability. Numerical simulations have been conducted to observe the emergence of spatial patterns and the impact of predator cooperation in these patterns. The study provides valuable insights into the dynamics of complex ecological systems, emphasizing the interplay of spatial and temporal factors in shaping population dynamics and predator-prey interactions.

Ecological and evolutionary dynamics of CRISPR-Cas systems in Clostridium botulinum: Insights from genome mining and comparative analysis

Understanding the prevalence and distribution of CRISPR-Cas systems across different strains can illuminate the ecological and evolutionary dynamics of Clostridium botulinum populations. In this study, we conducted genome mining to characterize the CRISPR-Cas systems of C. botulinum strains. Our analysis involved retrieving complete genome sequences of these strains and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Subsequently, we performed an analysis of homology in spacer sequences from identified CRISPR arrays to investigate and characterize the range of targeted phages and plasmids. Additionally, we investigated the evolutionary trajectory of C. botulinum strains under selective pressures from foreign invasive DNA. Our findings revealed that 306 strains possessed complete CRISPR-Cas structures, comprising 58% of the studied C. botulinum strains. Secondary structure prediction of consensus repeats indicated that subtype II-C, with longer stems compared to subtypes ID and IB, tended to form more stable RNA secondary structures. Moreover, protospacer motif analysis demonstrated that strains with subtype IB CRISPR-Cas systems exhibited 5′-CGG-3′, 5′-CC-3′, and 5′-CAT-3′ motifs in the 3′ flanking regions of protospacers. The diversity observed in CRISPR-Cas systems indicated their classification into subtypes IB, ID, II-C, III-B, and III-D. Furthermore, our results showed that systems with subtype ID and III-D frequently harbored similar spacer patterns. Moreover, analysis of spacer sequences homology with phage and prophage genomes highlighted the specific activities exhibited by subtype IB and III-B against phages and plasmids, providing valuable insights into the functional specialization within these systems.

Tracking and managing the water-food-environment-ecosystem (WFEE) nexus in groundwater irrigation districts using system dynamics modelling

Groundwater irrigation districts, which play a crucial role in the Earth's critical zone, are confronted with numerous challenges, including water scarcity, pollution, and ecological degradation. These issues come from multiple systems and are linked to a groundwater-dominated water-food-environment-ecosystem nexus problem related to agricultural activities (WFEE). There is a pressing need for the scientific characterization and evaluation of the WFEE nexus in groundwater irrigation districts to assure high-quality, sustainable development. Furthermore, it is critical to provide practical and efficient regulations at the farmer level to uphold the health of this nexus. This paper presents a mapping network that focuses on groundwater irrigation districts. The network aims to convert the restriction indicators utilized to maintain the health of the WFEE nexus (at the irrigation district scale) into the targets employed to manage farmers' living and agricultural activities (at the farmer scale). Additionally, a system dynamics model is created to track and manage the interacting relationships between the WFEE nexus and farmers' living and agricultural activities. This proposed model employs a structured parameter system comprising targets, state parameters, regulatory parameters, and evaluation parameters. This system can get insight into the status of the WFEE nexus at the farmer level using state parameters, induce tailored management and regulation measures using regulatory parameters, assess the effectiveness of various measures using the evaluation parameters, and finally provide decision support to enhance the health of the WFEE nexus. The findings from the research conducted in the Yong'an groundwater irrigation district demonstrated that the model accurately described the relationship between the WFEE nexus and farmers' activities in groundwater irrigation districts. Furthermore, the model responded strongly to a variety of improvement strategies, including adjustments in planting area, optimization of planting pattern, improvement of irrigation method, and implementation of agronomic measures. As a result, it provided farmers with decision support for applying agricultural management methods and addressing the WFEE nexus problem in groundwater irrigation areas.

ADP-Based Fault-Tolerant Control for Multiagent Systems With Semi-Markovian Jump Parameters.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

Connecting Soft and Hard: An Integrating Role of Systems Dynamics in Tsunami Modeling and Simulation

Connecting Soft and Hard: An Integrating Role of Systems Dynamics in Tsunami Modeling and Simulation