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Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton–Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader. [ABSTRACT FROM AUTHOR]

Research on agrifood supply chains has grown in recent years; however, little attention has been given to the effect of the choice of distribution channel on the sustainable and resilient performance of the network. This research proposes a system dynamics model to evaluate different distribution channels for a fresh food supply chain in a developing country. Among the main results is that the use of food hubs in a channel distribution produces long-term environmental, social and resilience benefits for the entire network, while short supply chains are the most economically viable alternative. [ABSTRACT FROM AUTHOR]

In September 2020, the European Parliament decided to include the CO2 emitted on the all intra-European and 50% of extra-European voyages in the European Union Emission Trading System (EU-ETS) starting from 2022, in order to reduce the CO2 emission. With the implementation of EU-ETS, the maritime supply chain will inevitably be significantly affected. Different carbon quota allocations in ETS have different impact on the CO2 emission reduction, which motivates us to investigate the effect of different carbon quota allocations on maritime CO2 emission reduction. Therefore, this paper considers two carbon quota allocations: the historical total quantity method and the historical intensity method, and develops a game model for each method. Then, the equilibrium solutions of each game model are analyzed. With the help of the system dynamics and the software Vensim, numerical experiments are conducted to verify the properties obtained in the analysis of the equilibrium solutions. The experimental results indicate that 1) Green technology (GT) can effectively reduce carbon emissions. Based on long-term simulation results, the GT investments will be gradually offset by profiting from carbon quota trading. 2) Profits may vary with price, but the Historical Intensity method consistently achieves better carbon reduction effects or relatively higher profits. 3) The marginal profits of companies with similar historical intensity baselines are almost the same. This gives an advantage to companies who implement green technologies earlier, as they can quickly gain returns from GT. • Comparing the effect of different carbon allocation methods on the maritime supply chain. • The historical intensity method reduces carbon emissions effectively, especially for companies with high unit emissions. • Using system dynamics to investigate the interaction of maritime supply chain. [ABSTRACT FROM AUTHOR]

• A novel composite parallel machining machine platform based on the structure of parallel robots is proposed. • A dynamic model method of the parallel machine platform based on geometric relationships is proposed. • The dynamic stress modelling approach of the parallel machine platform is given. In order to accurately analyze the structural characteristics of a composite parallel machine tool, a new structural response modelling approach is proposed in this paper. A system dynamics model of the novel composite parallel machining machine platform is established by using the generality of the Jacobian matrix. Based on the mechanical characteristics, a structural stress identification index is presented to reflect the structural response of the composite parallel machining machine platform. Based on the prototype of the parallel robot platform, numerical examples are presented and some performances of the structural response characteristics have been fully discussed. The proposed mathematical model and structural identification index can provide a convenient method for structural optimization of composite parallel machining platforms. [ABSTRACT FROM AUTHOR]

This paper takes a reaction-diffusion predator-prey system with ratio-dependent Holling III functional response function and Leslie-Gower term into consideration. First of all, the system model is proposed on the basis of basic biological assumptions and previous work, and the existence conditions of the equilibrium point of the system are discussed. Secondly, under the assumption of the existence of equilibrium point, the Turing instability necessary conditions induced by diffusion are investigated. Thirdly, optimal control theory is derived, and the adjoint system and the first-order optimization condition are established. Fourthly, parameter identification based on optimal control is studied, and the technique is extended to network structure. Finally, extensive numerical simulations, including Turing pattern, Normalized Population High Distribution Area (NPHDA) diagram and parameter identification, are carried out to illustrate and validate the analytical results. For the system dynamics phenomena, the results from different perspectives effectively demonstrate that the theoretical findings, numerical simulations and natural reality are identical. In terms of the parameter identification of continuous model and network model, the efficiency and accuracy of various algorithms are fully tested. • This paper proposes and investigates index normalized population height distribution area. • The optimal control problem of reaction-diffusion predator-prey system is formally derived. • The parameter identification research is extended to population network structure space. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies. • Addressing systems with nonlinear inputs, states, and output dynamics, and modeling them using NN approximators. • Handling unknown and maybe potentially unbounded additive uncertainties. • Introducing a new model for all nonlinearities and sensor uncertainties and augments the system as a descriptor system. • Designing an observer to concurrently estimate the state of the original system and measurement uncertainties. • Using the MRC technique to handle stable and unstable systems with unknown parameters, ensuring the desired performance. [ABSTRACT FROM AUTHOR]

The paper suggests a research framework that can aid simulation model designers and users in understanding and modeling complex concepts. The aim is to enhance the role of simulation in supporting decision-making with agent-based modeling and system dynamics by investigating literature that outlines challenges in the field. This study advocates for involving decision-makers in unpacking the overarching concept into manageable components that are associates with the top concept. The research framework generates insights into the interpretation of complex concepts and guides model designers in formulating concrete variables for simulation modeling through successive iterations of the unpacking practices. [ABSTRACT FROM AUTHOR]

• A semi-analytical method is adopted for arbitrary fluid-conveying spatial pipeline. • The actual flexible-bracket clamp stiffness is considered. • Virtual springs are used for the straight-curved connection. In-depth research is imperative for complex spatial pipelines with fluid-structure interaction. A comprehensive and universal three-dimensional semi-analytical modeling method is first established for complex fluid-conveying spatial pipelines under the base excitation with arbitrary connections and configurations under different boundary conditions. The virtual spring technology is used to connect the straight and curved pipeline segments with spatial angles and simulate boundary conditions. For a thorough analysis, the actual stiffness of the bracket-clamp system is experimentally measured for the first time. The multi-level coupling dynamic model of part, component, and system levels under actual boundary conditions is established. An efficient method for solving the fluid-structure coupling pipeline system dynamics considering the base excitation is proposed based on energy method. The mid-plane displacement function of the Fourier series is introduced and solved using the principle of modal superposition. Hammer impact modal experiments, ANSYS software and vibration response experiments are performed on spatial pipelines supported by flexible bracket-clamps; the numerical and experimental results support the semi-analytical results. The degenerate model is verified with the simple plane pipelines in other references, and the errors are less than 5 %. The proposed method exhibits broad applicability and generalizability, making it suitable for various configurations and boundary conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Insights into the waste electrical and electronic equipment (WEEE) management. • Developed a generic system dynamics model simulating the stock and flow of WEEE. • Applied the model to eleven WEEE categories in the United Kingdom. • Sensitivity and backcasting scenario analyses of landfilled WEEE targets. • Identified enhancing collection rates as the best way to reduce landfilled WEEE. Every year an estimated two million tonnes of waste electrical and electronic equipment (WEEE) are discarded by householders and companies in the United Kingdom (UK). While the UK has left the European Union (EU), its waste-related policies still mirror those of the EU, including the WEEE-related policies. Motivated by the recent introduction the so-called 'Right to Repair' policy for electrical and electronic equipment (EEE) across the EU and UK, this paper aims to demonstrate that, depending on the commitment and behavioural changes by the consumers and the government, the future of the WEEE management of the UK will vary. To this end, focusing on landfilled WEEE reduction we develop a generic system dynamics model and apply it to eleven WEEE categories. They depict the flow of EEE and WEEE representing the interaction among the stakeholders (e.g., consumers and producers of EEE) and relevant government regulations of the UK. Our four scenario analyses find that longer use of EEE and better WEEE collection seem to be effective in reducing landfilled WEEE, while more reuse and more recycling and recovery have negligible impacts, despite excluding the additional generation of landfilled WEEE as a result of recycling and recovery. Comparing with the business-as-usual scenario, one year longer EEE use and 10% more of WEEE collection could at maximum reduce landfilled WEEE by 14.05% of monitoring and control instruments and 93.93% of display equipment respectively. Backcasting scenario analyses reveal that significant efforts are required to reduce the targeted amounts. [ABSTRACT FROM AUTHOR]

Due to the acceleration of global environmental change and urbanization, landscape sustainability assessment and optimization have become key tasks in urban planning and management. Computer simulation technology, especially System Dynamics (SD) models, can simulate the dynamic behavior of landscape systems and provide scientific basis for decision-making. This study used the SD model to construct a landscape sustainability assessment model. Firstly, key factors affecting landscape sustainability, such as land use, water resource management, and biodiversity conservation, were identified through literature review and expert interviews. Then, the Causal Loop Diagram (CLD) was used to analyze the interactions between these factors. Next, the model in SD modeling software was built; parameters and initial conditions were set; model validation and calibration were performed. Research has found that the SD model can effectively simulate the dynamic changes of landscape systems, providing a comprehensive, dynamic, and quantitative method for landscape sustainability assessment. In addition, compared to traditional management strategies, the ecological service value of the same location under the SD method could increase by 91€; the Shannon Wiener index could increase by 0.97; the soil organic matter content could increase by 11.27g/kg. The comparative experimental data results indicated that the SD model had significant advantages in landscape sustainability assessment and optimization, and could provide sustained support for landscape sustainability. Therefore, computer simulation technology has important application value in landscape sustainability assessment and optimization. [ABSTRACT FROM AUTHOR]

Previous research on tolerance analysis has primarily overlooked the influence of mechanical vibration on the accuracy of motion, neglecting its significance in systems with moving components. To bridge this research gap, we propose an innovative tolerance analysis framework that combines the theories of skin model shapes and system dynamics. The skin model shapes are employed to represent the form deviations of the workpiece, while the principles of system dynamics are utilized to describe and analyze dynamic behavior. Then, we conduct simulation studies on cam mechanisms to show tolerance analysis outcomes with the consideration of mechanical vibration. This study also defines an index to measure the impact of internal mechanical system components on tolerance analysis. Our findings indicate that the presence of dampers reduces the effect of surface deviation of parts on kinematic accuracy, which is associated with the value of damping coefficient. Therefore, when the internal damping effect of the system is considered in the tolerance analysis, the tolerance design requirements of the parts can be appropriately relaxed, thereby reducing the cost. • A skin model-based framework for tolerance analysis is proposed to achieve enhanced accuracy in analysis results. • Integrating mechanical vibration in tolerance analysis improves real-time error prediction. • The effectiveness of the framework is demonstrated through its application to a snail cam mechanism. [ABSTRACT FROM AUTHOR]

Normal-stressed electromagnetic actuator (NSEA) has been demonstrated promising for nanopositioning stages running in hundreds of micrometers. However, system nonlinearities and their influences on the actuation behavior in NSEA have not been well revealed, blocking the in-depth understanding and high-performance application of the NSEAs. This paper developed an intrinsic electromagnetic actuation model by systematically including the nonlinear position-varying flux leakages, nonlinear flux density related to the field strength (B-H curve), and magnetic saturation. Based on the model, the influences of those nonlinear factors on the actuation were characterized theoretically. Experiments well verified the theoretical model and further characterized the dynamic hysteresis in the actuation. Finally, a phenomenological Wiener model cascading linear system dynamics with a least-squares support-vector machine (LS-SVM) model was developed for the feedforward compensation for system nonlinearities. The maximum modeling error was less than ± 4 % for the excitation with frequency ranging from 25 Hz to 200 Hz. Tracking of a complex trajectory combining multiple harmonics with various frequencies and randomly chosen amplitudes exhibited a maximum tracking error of ± 0. 7 μ m , which was only 28% of that without compensations. The results demonstrated the effectiveness of the proposed nonlinear compensation strategy for NSEAs. [Display omitted] • Intrinsic factors dominating the actuation nonlinearity are investigated for NSEAs. • Effect of the system nonlinearity on its actuation is theoretically investigated. • The nonlinearity for a NSEA stage is demonstrated experimentally. • A phenomenological model was developed for describing the system nonlinearity. [ABSTRACT FROM AUTHOR]

This work proposes a non-smooth epidemic model with a two-threshold control strategy. Under the two-threshold policy, social distancing is suggested when the infection level exceeds a certain threshold, and contact tracing is implemented and isolation rate is improved when the infection level exceeds a higher threshold. The global dynamics of the non-smooth system is investigated and it reveals that the disease-free equilibrium, or one of the three endemic equilibria of the subsystems, or one of the two pseudo-equilibria may be globally asymptotically stable, which depends on the two threshold levels and the intensity of control measures. The global stability of the pseudo-equilibrium indicates that the disease could be maintained at a previously given level by choosing the two thresholds properly. It is also obtained that at low thresholds and high intensity of control measures, the peak value is low and peak time is late for the unisolated infections and daily new infections, and the number of cumulative infections is small. However, the effects of control measures are very limited both on the final trend of the epidemic and the cumulative infections if threshold levels are too high. • A non-smooth epidemic model with a two-threshold control strategy is proposed. • Disease-free equilibrium, or endemic equilibrium, or pseudo-equilibrium may be globally asymptotically stable. • The disease could be maintained at a previously given level for proper thresholds. • Low thresholds and high implementation intensity of control measures are beneficial for disease control. • Effects of control measures are very limited if thresholds levels are too high. [ABSTRACT FROM AUTHOR]

The rapid development of small and medium-sized logistics enterprises has also revealed potential credit risk issues. Based on this, this paper constructs a system dynamics model to simulate the key factors affecting the credit risk of small and medium-sized logistics enterprises. Firstly, the paper analyzes the factors affecting the credit risk of small and medium-sized logistics enterprises from multiple aspects such as industry characteristics and sources of risk, and constructs an index system for these factors. Secondly, the paper uses the system dynamics method to build a mechanism model of credit risk for small and medium-sized logistics enterprises, and deeply analyzes the factors affecting the mechanism of these enterprises. Finally, through simulation analysis of examples, the paper identifies key risk factors and proposes effective control measures to improve the overall credit risk situation of small and medium-sized logistics enterprises. [ABSTRACT FROM AUTHOR]

Amidst the rapid pace of globalization and the rising density of populations, the prevalence of epidemic outbreaks has emerged as a heightened cause for apprehension. The emergence and spread of epidemic are determined by the coupling of multiple factors. Herewith, the method of text mining was used to extract these factors, including vaccination, detection, and physical protection. An evolution model considering multiple factors based on system dynamics theory was established to simulate epidemic dynamics. The results of numerical experiment showed that this model would effectively nowcast the epidemic dynamics. The heterogeneous efficacy of multiple interventions on containing the epidemic was estimated, where personal protection, the vaccination rate, and isolation control had negative feedback effects on epidemic evolution; physical protection showed the strongest effect while isolation presented the weakest ones. On this basis, countermeasures to effectively control the outbreak are summarized. [ABSTRACT FROM AUTHOR]

Neural network (NN) controllers have shown great potential in solving complex control or decision-making tasks. However, most of the NN controllers either rely on the availability of large datasets or require dense interactions with the environment, which hinders their application in real systems. In this paper, we introduce a model-based reinforcement learning (MBRL) algorithm, aimed at realizing ultra-fast tuning of deep NN controller from a small sample set of real-world data. The algorithm uses Gaussian processes (GPs) to model the unknown dynamics of real system and updates controller parameters through stochastic gradient descent. By using particle-based method for long-term predictions, the algorithm can easily incorporate online state estimators and filters into controller learning, which is conductive to learning from systems with partially measurable states and stochastic control delay. We apply the algorithm to calibrate a deep NN controller for the path tracking of a full-size autonomous vehicle (AV). Simulation and field test results show that the deep NN controller can be well calibrated after only one interaction with the environment and can achieve similar tracking performance to optimization-based methods such as nonlinear model prediction control (NMPC) in various test scenarios by combining with a feed-forward pure pursuit (PP) controller. • Ultra-fast tuning of deep NN controller is achieved under the framework of probabilistic MBRL. • The deep NN controller incorporating state estimator has been applied to the path tracking of autonomous vehicle. • The proposed algorithm for calibrating deep NN controller has been validated through various simulation and field tests. [ABSTRACT FROM AUTHOR]

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber–physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end. • The augmented time-varying value function (TVRE) can be represented as the time-varying quadratic form is demonstrated. In conjunction with this finding, the time-varying Riccati equation for the finite-horizon optimal secure tracking control problem is derived. • The impact of the DoS attacks on the solution of the TVRE is demonstrated. The convergence of the TVRE is proved under certain assumptions. Furthermore, a lower bound on the probability of DoS attacks that satisfies these assumptions is derived. • A time-varying Q-function (TVQF) is designed. The equivalence between the approximation of the TVQF kernel matrix and the solution of the derived TVRE is proved. Additionally, a TVQF-based model-free optimal secure tracking control algorithm is developed. [ABSTRACT FROM AUTHOR]

The launcher recovery method known as 'In-Air Capturing' is an innovative approach where a winged rocket stage is captured mid-air by an aircraft and subsequently towed back to the launch site. To achieve this, a capturing device attached to a tether is released from the towing aircraft. This device autonomously connects the reusable launch vehicle to the aircraft, while the two vehicles are in proximity. The tether's flexible dynamics have a strong influence on the maneuverability of the capturing device. Thus, it is critical to model the dynamics of the tether to get a realistic understanding and to evaluate feasibility of the concept. However, precise modelling and control of highly flexible systems with large deformations is both challenging and computationally intensive. For engineering applications which involve closed-loop simulations, finding a trade-off between accuracy and computational effort is crucial. In this paper, the tether is modelled as a discretized chain of rigid bodies connected by rotational springs at the joints. The tether along with the capture system is integrated in a simulation model and open loop tests are performed to analyse the system characteristics. The tether properties are studied to find a suited configuration for 'In-Air Capturing'. • Multibody modelling approach for long cable like bodies with large deformation. • Capture system dynamics for the unique 'In-Air Capturing' launcher recovery concept. • Dynamic modelling for the complete capturing system including towing aircraft, capturing device and tether. • Assessment of tether properties suited for 'In-Air Capturing' with sensitivity studies. [ABSTRACT FROM AUTHOR]

We establish a bus market evolution model to explore the implications of subsidy policies in Hong Kong (HK) for the transition to a zero-emission bus (ZEB) fleet using an unsubsidized case as the benchmark scenario. Using the system dynamics (SD) approach, we capture the intricate feedback mechanisms among market share dynamics, cost considerations, consumer preferences, and competitive interactions within the bus market. These interactions are encapsulated within three pivotal feedback loops: scale economy, public acceptance, and bus market competition. We systematically integrate the HK-specific features of hydrogen buses (H-buses), battery electric buses (E-buses), and diesel buses (D-buses) from technical and economic perspectives and compare them to the benchmark scenario. The results show that only 66% of the bus fleet would be ZEBs by 2050, and this underscores the necessity for governmental financial incentives to expedite the transition to a fully zero-emission fleet. Furthermore, we evaluate various ZEB subsidy policy alternatives in terms of timing, intensity, and recipients. The analysis suggests that policies offering long-term and low-intensity subsidies are most beneficial, taking into account the cumulative effects and inherent delays associated with policy implementation. Importantly, the study highlights the critical balance that policymakers must achieve between short- and long-term objectives, especially given the counter-intuitive outcomes observed in the early stages of policies favoring H-buses exclusively. This study provides valuable policy insights for policymakers and upstream energy companies in HK, with potential applicability in wider real-world contexts. • Zero-emission bus alternatives are analyzed from technical and economic aspects. • Various feedback loops are considered for dynamic market analysis. • A counter-intuitive effect is observed in the hydrogen bus subsidy scenario. • A long-term subsidy with low intensity is recommended. • Tradeoff between short- and long-term policy effects is identified and discussed. [ABSTRACT FROM AUTHOR]

Immunization programs globally have played a pivotal role in reducing mortality rates. However, the effectiveness of these programs often falls short of the intended coverage targets, primarily due to challenges within the supply chain, storage facilities, and distribution processes. Notably, a substantial percentage of vaccine wastage has been observed in numerous countries, thereby imposing a significant financial burden on immunization initiatives, and presenting a formidable challenge to policymakers. This paper undertakes a comprehensive study of the financial dynamics inherent in an immunization program within the context of India. Employing a systems perspective, we scrutinize the intricate interactions among various cost factors. Through a five-year system dynamics simulation, we derive insights into the amplified financial constraints resulting from vaccine wastage and the inefficiencies associated with shared personnel. Furthermore, our analysis extends to evaluate the impact of an additional investment aimed at enhancing the quality of the cold chain infrastructure and health worker proficiency on the overall funds of the immunization program. This research contributes to a nuanced understanding of the financial intricacies of immunization programs, offering valuable insights for policymakers and stakeholders seeking to optimize program efficiency and resource allocation. [ABSTRACT FROM AUTHOR]

In this work, we focus on a binary stellar system consisting of two elliptical galaxies. Specifically, our investigation centers around prolate and oblate elliptical galaxies, which we model using the circular restricted three-body problem. Employing various standard numerical methods, we determine the positions of equilibrium points and analyze their linear stability and dynamical characteristics. To comprehensively explore the influence of mass and shape on the equilibrium points and their linear stability, we systematically investigate and discretize the parameter space of the system. Our study reveals a consistent pattern of an odd number of equilibrium points, which varies from 5 to 11. Notably, the system consistently displays linearly stable points, with the number of collinear points varying between 3 and 5. [ABSTRACT FROM AUTHOR]

Water electrolyzers will ensure energy security and power grid stability in energy systems based on fluctuating renewable energy sources such as wind power and photovoltaics. In this article, the effects of volatile energy generation on the operating conditions within polymer electrolyte membrane water electrolyzers are investigated. Based on experiments on a 100 kW el test station, it is concluded that isothermal and isobaric operation is unattainable and temperature deviations between −7 K and 3 K from their setpoint are observed. Pressure control on the hydrogen side is more challenging than on the oxygen side. Temperature decreases due to non-ideal temperature control overall decrease the mean efficiency during dynamic operation as compared to steady-state, with wind power reaching 73.70 % LHV and solar power yielding 71.90 % LHV. Finally, adaptions of electrolyzer designs and operation strategies are discussed, that could reduce negative effects of system dynamics on process efficiency. • System analysis is performed to identify inherent bottlenecks to dynamic operation. • Experimental investigation of characteristic load profiles on 100 kW el electrolyzer. • Evaluation of operation profiles reveals PV to be more challenging than wind power. • Dynamic operation appears to lower the overall hydrogen production efficiency. • Potential strategies to mitigate negative effects of system dynamics are discussed. [ABSTRACT FROM AUTHOR]

High Density Polyethylene (HDPE) is a highly recyclable thermoplastic comprising 17% of all plastic produced. Yet, demand for recycled HDPE (r-HDPE) has outrun availability, partly due to manufacturers' interest in meeting sustainability goals and recycled content mandates. Given that HDPE has a variety of uses, understanding optimal allocation of the constrained r-HDPE supply across product categories and lifespans will support the challenge of addressing broader sustainability goals and establishing circular economy (CE). By utilizing systems dynamic (SD) and life cycle analysis (LCA) modeling, this work studies whether prioritizing r-HDPE for longer-life products is more environmentally beneficial, compared to shorter-life products (e.g., food-grade packaging). Specifically, this work compares the production of milk containers and drainage pipes using virgin and r-HDPE as examples of HDPE applications through three scenarios. Key findings emerged from this study including the demonstration of a slight advantage to prioritizing allocating the constrained r-HDPE supply to the longer-life product vs the shorter-life product and a distinct challenge in hitting recycled content targets with current recycling rates and 2030 goals for the United States. This highlights a need to expand current recycling infrastructure in tandem with other CE practices to reduce plastic consumption and sustain environmental health for all. [ABSTRACT FROM AUTHOR]

The passenger vehicle sector has been identified as a major contributor to carbon emissions in China. Accurately estimating carbon emissions is crucial, particularly in light of China's ambitious goal to reach its carbon emissions peak by 2030. Previous life cycle assessment (LCA) studies on single-passenger vehicles rarely consider technological advancements to accurately predict future carbon emissions in the entire fleet. As such, a fleet-based life cycle approach that integrates input-output-based hybrid LCA (IOH-LCA) and system dynamics is proposed to simulate the evolution of carbon emissions in the passenger vehicle sector from 2016 to 2035. This method captures the complex changes in the sets of vehicles produced for sale, in use, and disposed of over time, as well as the multiple time-dependent technological advancements. A scenario analysis is constructed to analyze carbon emissions in alignment with the commitment to achieving the carbon emissions peak by 2030. The results indicate that, in a positive scenario with the combined advancement of multiple technologies, the passenger vehicle sector will be able to peak carbon emissions before 2030. This study could provide support for policymaking regarding carbon reduction in China's electrification of passenger vehicles. [ABSTRACT FROM AUTHOR]

The present investigation, whose main objective is based on modeling the hospital logistics capacity in the Pasco Healthcare Network (Peru) through Systems Dynamics during the first and second wave of the appearance of the SARS-COVid-2 virus, oriented towards a study in the capacity to respond to the most intense health emergency in recent decades. In the first instance, the reality of the studied system was made known, which was possible thanks to the information provided by the actors that make up the hospital logistics capacity, sources uploaded to the WHO worldwide. Afterwards, we proceeded to characterize the variables that make up the system, prioritizing each of these through the application of a structural analysis that measured the levels of influence between one variable and another. Because of this, we proceeded to create the causal diagram, and likewise facilitate the inclusion of the network model through the Stella software. Once the model has been simulated, the results obtained are reported. [ABSTRACT FROM AUTHOR]

In this paper, a distributed bearing-based formation control scheme with finite-time convergency is proposed for multiple underactuated surface vessels (USVs). By virtue of the guide point-based model transformation method, the dimension of underactuated tracking error dynamics can be reduced from three to two. This allows us to convert the actuator model to a fully form that matches dimension-reduced tracking error dynamics. Further, to reduce the computation complexity, a finite-time recruited filter is designed according to first-order Levant differentiator. At meanwhile, auxiliary error compensation signals are introduced to address unknowns stemming from filter process and system dynamics. Since merely relative-distances and inner bearings are utilized in the proposed bearing-based formation approaches, the proposed control scheme provides a feasible solution to achieve formation control under body-fixed frame. Finally, theoretical analysis and numerical simulations are presented to demonstrate the efficacy. • Bearing-based finite-time formation control. • Underactuated surface vessels. • Levant differentiator. • Command filtered backstepping with error compensation signals and compensated errors. [ABSTRACT FROM AUTHOR]

In this study, we investigate a system comprising of two Kerr-like bodies within the context of the planar circular restricted three-body problem. Our main objective is to explore how the equilibrium dynamics of the system are influenced by the free parameters present in the potential. To achieve this, we employ a combination of semi-analytical and numerical methods. Specifically, we numerically compute the coordinates, linear stability, and types of the libration points on the (x , y) plane. Additionally, we extend the analysis beyond a previously published paper by considering the general case where the main bodies of the system have different transition parameter values. In other words, the parameters that control the ratio between classical Newtonian and post-Newtonian gravity in each main body vary. Our findings reveal that this new setup, in contrast to the scenario with equal transition parameter values, exhibits a minimum of five equilibrium points, while the maximum number of equilibria remains unchanged at thirteen. [ABSTRACT FROM AUTHOR]

In this work, the dynamics for coupled system of dispersive nonlinear Schrödinger equation with Kudryashov's law of nonlinearity are studied with the aid of improved modified extended tanh function method. This model is used to mimic the wave propagation through magneto-optic wave guides. Various types of solutions are extracted for the proposed model. These solutions including dark solitons, bright solitons and singular solitons, Weierstrass elliptic and singular periodic solutions. Moreover, the graphical illustrations of some solutions are introduced to demonstrate the nature of them. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for regulating the pose of a free-flying dual-arm anthropomorphic space manipulator system (SMS) using a finite-time state-dependent Riccati equation (SDRE) controller. The proposed system finds applications in on-orbit satellite inspection, servicing, space structure assembly, and debris manipulation. The dual-arm SMS presented in this work consists of two 7 degrees of freedom (DoF) robotic arms mounted on a free-flying spacecraft, resulting in a complex 20-DoF system. Due to the high number of DoFs, advanced controller design and efficient computations are necessary. The finite-time SDRE controller relies on the state-dependent coefficient (SDC) parameterization matrices, which are nonlinear apparent linearizations of the dynamics. Conventionally, the computation of SDC matrices is offline and relies on the a priori derivation of the analytical equations governing the dynamics of the system. However, this strategy becomes computationally impractical for high DoF plants. To overcome this issue and deliver a more viable solution, a numerical method to construct and update the SDC matrices at each time step is presented. This approach relies on a screw-theory-based recursive Newton–Euler algorithm designed to reconstruct the manipulator inertia and Coriolis matrices. These quantities are the building blocks of the SDC parameters used in the synthesis of the SDRE controller. Simulation results demonstrate the performances of the finite-time SDRE controller augmented with the online update of the state-dependent coefficients. • Automatic derivation of the dynamics of free-flying robot with multiple open chains. • Implementation of a Newton-Euler approach for updating state-dependent coefficients. • Finite-time SDRE to control a 20 DoFs, anthropomorphic dual-arm space manipulator. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamics of a discrete FitzHugh-Nagumo (FHN) model with self-diffusion on two-dimensional coupled map lattices. The primary objective is to analyze the complex dynamics of neuronal systems in a discrete setting. Through the application of central manifold and normal form analysis, it has been demonstrated that the system is capable of undergoing Neimark-Sacker and flip bifurcations even in the absence of diffusion. Additionally, when influenced by diffusion, the system can manifest pure Turing instability, Neimark-Sacker-Turing instability, and Flip-Turing instability. In the numerical simulation section, the path from bifurcation to chaos is explored by calculating the maximum Lyapunov exponent and drawing the bifurcation diagram. The interconversion between various Turing instabilities is simulated by varying the values of the time step and the self-diffusion coefficient. This study contributes to a deeper understanding of the complexity of neural network systems. • A time-space discrete FitzHugh-Nagumo (FHN) model base on coupled map lattices is considered. • Conditions of Neimark-Sacker-Turing instability and Flip-Turing instability are obtained. • The path from bifurcation to chaos is given by calculating the maximum Lyapunov exponent. • The mutual transformation in various Turing patterns can be realized by changing some parameters. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of a vehicle fleet is a complex task that can be identified as having multiple variables and nonlinear feedback loops influenced by different mobility, socio-economical, and environmental factors. Due to the prevailing concerns on the fuel economy and vehicular emissions, formulating a clear policy for electric and other alternative mobility forms requires a clear decision support tool to adequately consider the complexities involved. This paper presents a novel System Dynamics (SD) approach based on the cause-and-effect analysis and feedback loop structures as a suitable tool. The proposed SD model comprises four sub-models: socio-economic, new vehicle registrations, environmental influence, and fuel economy and e-mobility policy measures. The study presents the analysis of historical data and results from the impact analysis of fuel economy policies in Sri Lanka, including the introduction of electric mobility. The authors leveraged the Vensim SD software package for modelling, expert-in-the-loop sensitivity analysis and optimization. Three strategic fuel economy and e-mobility policy intervention scenarios have been developed to estimate the forecasted fuel economy for Four-Wheeled Passenger Vehicles (FWPVs) as per strategic interventions to change the fuel type of new vehicles registered. It is shown that hybrid and electric FWPVs can improve fuel economy by between 13 and 39% by 2030. • A System Dynamics approach is proposed to model the energy efficiency of Four-Wheeled Passenger Vehicles' registrations. • Four sub-models are developed to report the impacts of fuel economy and e-mobility policies. • Three policy scenarios have been formulated to test the application of a target of reaching 50% of the conventional fuel registrations. • The effective change of fuel economy improvement under three scenarios vary between 13 to 39%. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to leverage generative adversarial networks(GANs) techniques to learn a feedback linearization controller(FLC) for a class of uncertain nonlinear systems. By estimating uncertainty through the adversarial process, where ground truth samples are exclusively obtained from a predefined integral model, the feedback linearization controller, learned through a minimax two-player optimization framework, enhances the reference tracking performance of the input-output uncertain nonlinear system. Furthermore, we provide theoretical guarantee of convergence and stability, demonstrating the safe recovery of robust FLC. We also address the common challenge of mode collapse in GANs training through the strict convexity of our synthesized generator structure and an enhanced adversarial loss. Comprehensive simulations and practical experiments are conducted to underscore the superiority and efficacy of our proposed approach. • Use GANs for feedback linearizationn in nonlinear systems with unknown dynamics. • Provide rigorous proof for GANs' convergence and stable feedback linearization in closed-loop systems. • Solve GANs' mode collapse and instability with modified adversarial loss and convex objective. • Test our GANs-driven feedback linearization controller on a flight simulator. [ABSTRACT FROM AUTHOR]

Maintaining the reliability of the modern power system is becoming increasingly challenging due to the continuous growth in its size and complexity. One of the most significant challenges PS faces is frequency regulation, which is essential for maintaining a stable and reliable operation of PS. This article investigates the automatic generation control (AGC) of a multi-area PS considering partial loading scheduled in a hydro and thermal generating unit. A bird swarm algorithm (BSA) based two-degree of freedom tilt integral derivative (2DOF-TID) controller has been suggested. The superiority of the optimal 2DOF-TID controller has been selected by comparing the system dynamics with other conventional controllers. Also, the ascendancy of suggested BSA has been shown over the grasshopper algorithm and particle swarm optimization techniques. The investigation on partial loading schedules for the hydro-thermal system showed that the system's dynamic response deteriorates when loading on hydro units exceeds their nominal loading. Also, system dynamics are enhanced when thermal units are loaded beyond their capacity. The investigations are also conducted to analyse the effect of high voltage direct current (HVDC) link and virtual inertia emulation with an energy storage system. The studies with alternating current (AC)/HVDC and virtual inertia revealed that dynamic responses are better with the latter when compared with AC and HVDC links alone. • A novel 2DOF-TID controller has been proposed for a three-area hydro-thermal AGC system. • The controller gains and other parameters are optimized using a novel meta-heuristic algorithm called the BSA. • The supremacy of BSA has been analyzed over PSO and GHA. • The performance of the system, considering different loading schedules in thermal and hydro generating units is analyzed. • The effects of a parallel AC/HVDC link and virtual inertia with RFB on system dynamics are analyzed. [ABSTRACT FROM AUTHOR]

This study develops a methodology using a system dynamics approach to analyse human error probability (HEP) within the context of hydrogen fuelling station (HFS) maintenance, mainly focusing on the dynamic nature of performance shaping factors (PSFs) over time. The developed model offers a comprehensive understanding of how diverse human factors dynamically influence task performance, yielding critical insights for safety management strategies to study 8-hour day shifts in maintenance activity. The study explores the intricate relationship between time-dependent PSFs and human error probability, highlighting that as the number of maintenance tasks rises, so does the potential for increased fatigue levels, subsequently elevating HEP. Introducing breaks during work emerges as a promising intervention to mitigate task-related fatigue, reducing HEP. Careful break implementation is necessary to prevent shifts from extending, which could inadvertently raise HEP. This research has implications extending beyond HFS, benefiting industries where operational safety and efficiency are paramount. Future studies can build upon these findings, exploring additional interventions and diverse work scenarios to advance our understanding of human performance and error prevention strategies, ultimately fostering safer and more productive work environments. The integration of system dynamics and the insights gained contribute significantly to hydrogen safety and offer a robust foundation for future investigations in this field. • Impact of time-related factors was studied on human error probability in hydrogen fuelling stations. • A new methodology was developed, including causal loop and stock-flow diagrams. • The model shows that 15-min breaks every 2 h reduced HEP by 20 %. [ABSTRACT FROM AUTHOR]

Carbon dioxide is an important component and one of the contributors to greenhouse gas emissions. Taiwan has set new goals to reduce emissions by 23%-25% in 2030. However, electricity production in 2021 is still contributed by coal-fired power plants. This research explores potential carbon pricing strategies to decarbonize coal-fired power plants, which are carbon fees and carbon emission trading. This research uses a system dynamics framework for causal loop diagram development. This research creates a causal loop diagram to describe the carbon pricing strategy's impact on reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Taiwan has gradually increased its electric vehicle (EV) market share over the past few years. The increasing availability of affordable EV models has also contributed to the rising market share of EVs in Taiwan. This study provides a system dynamics model for evaluating the impact of various factors on the electric vehicle market share in Taiwan. The model incorporates key variables such as consumer preferences, financial acceptability, charging infrastructure and accessibility, EV performance, and customer acceptance to simulate the dynamic behavior of the EV market. Moreover, the model utilizes historical data and the government's target plan to calibrate and validate a model that accurately captures the current state of Taiwan's electric vehicle (EV) market share. Results indicate that charging stations and financial issues impact EV adoption significantly. Overall, this study demonstrates the usefulness of system dynamics modeling in evaluating policy interventions and identifying critical drivers for promoting the transition to a greener transportation system. [ABSTRACT FROM AUTHOR]

Chili pepper is an important commodity with 34.4% of its production in Indonesia coming from the province of East Java. However, the productivity of chili pepper is still below the national average, even with the largest harvested area. This study aims to find alternative solutions using a system dynamics approach to increase the productivity of chili pepper. The scenario results show that the application of Good Agricultural Practices (GAP) will increase the average productivity by 16.41%. [ABSTRACT FROM AUTHOR]

Food security remains a critical concern in Indonesia, prompting the need for adequate food availability nationwide. Maize, as a prominent staple crop, faces demand imbalances across the country. To address this issue, a system dynamics model is developed to analyze the dynamics of maize supply and demand in East Java. The model captures the non-linear and dynamic relationships inherent in maize production and consumption. Through a rigorous research methodology, this study presents a comprehensive system dynamics model that depicts the current state and dynamics of maize supply. The key findings shed light on the factors influencing maize availability in East Java and provide insights for policymakers to enhance food security. By utilizing a systems approach, this research contributes to the understanding of maize supply dynamics and supports the development of strategies to ensure sufficient food supply in the region. [ABSTRACT FROM AUTHOR]

The soft manipulator actuated parallel with contractile and extensor pneumatic artificial muscles (PAMs) can perform continuous operations and vary stiffness. Due to the hysteresis nonlinearity of PAMs and parallel driving, the manipulator shows coupled hysteresis with stiffness-dependent and rate-dependent characteristics, causing inaccurate positioning performance. This paper firstly proposes a comprehensive dynamic model consisting of two parts in series to predict the manipulator dynamical behaviors with coupled hysteresis. Furthermore, a decoupled inverse compensation and decoupling control on the basis of the proposed model is conducted on the soft manipulator. Experimental results and comparisons illustrate the high precision and strong generalization ability of the proposed model and the validity of the compensator in mitigating the coupled dynamic hysteresis of the system. • A coupled dynamic hysteresis model is proposed for the PAM-based manipulator system. • The model is based on UPI operators and the fuzzy recurrent neural network. • An inverse compensation control is developed to decouple and compensate the system. • Validations of the proposed model and compensation method are conducted. [ABSTRACT FROM AUTHOR]

In this paper, an innovative event-triggered optimal tracking control algorithm is proposed for input saturated strict-feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid-triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event-triggered optimal controller. Therefore, the final controller consists of a hybrid-triggered feedforward controller and an event-triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization-oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer-controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event-triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high-order robot system and marine surface vehicle to demonstrate its effectiveness. • A novel event-triggered optimal tracking control framework is proposed. • A phenomenon we defined as the "jump of error surfaces" is noticed and tackled via filter technique. • A multi-error based adaptive triggering rule is established to improve the control performance. • The proposed method not only achieves the optimality but also lowers data transmission. [ABSTRACT FROM AUTHOR]