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We present a fully quantum dynamical treatment of a quantum heat engine and its baths based on the Hierarchy of Pure States (HOPS), an exact and general method for open quantum system dynamics. We show how the change of the bath energy and the interaction energy can be determined within HOPS for arbitrary coupling strength and smooth time dependence of the modulation protocol. The dynamics of all energetic contributions during the operation can be carefully examined both in its initial transient phase and, also later, in its periodic steady state. A quantum Otto engine with a qubit as an inherently nonlinear work medium is studied in a regime where the energy associated with the interaction Hamiltonian plays an important role for the global energy balance and, thus, must not be neglected when calculating its power and efficiency. We confirm that the work required to drive the coupling with the baths sensitively depends on the speed of the modulation protocol. Remarkably, departing from the conventional scheme of well-separated phases by allowing for temporal overlap, we discover that one can even gain energy from the modulation of bath interactions. We visualize these various work contributions using the analog of state change diagrams of thermodynamic cycles. We offer a concise, full presentation of HOPS with its extension to bath observables, as it serves as a universal tool for the numerically exact description of general quantum dynamical (thermodynamic) scenarios far from the weak-coupling limit. [ABSTRACT FROM AUTHOR]

Two-level quantum systems are fundamental physical models that continue to attract growing interest due to their crucial role as a building block of quantum technologies. The exact analytical solution of the dynamics of these systems is central to control theory and its applications, such as that to quantum computing. In this study, we reconsider the two-state charge transfer problem by extending and using a methodology developed to study (pseudo)spin systems in quantum electrodynamics contexts. This approach allows us to build a time evolution operator for the charge transfer system and to show new opportunities for the coherent control of the system dynamics, with a particular emphasis on the critical dynamic region around the transition state coordinate, where the avoided crossing of the energy levels occurs. We identify and propose possible experimental implementations of a class of rotations of the charge donor (or acceptor) that endow the electronic coupling matrix element with a time-dependent phase that can be employed to realize controllable coherent dynamics of the system across the avoided level crossing. The analogy of these rotations to reference frame rotations in generalized semiclassical Rabi models is discussed. We also show that the physical rotations in the charge-transfer systems can be performed so as to implement quantum gates relevant to quantum computing. From an exquisitely physical–mathematical viewpoint, our approach brings to light situations in which the time-dependent state of the system can be obtained without resorting to the special functions appearing in the Landau–Zener approach. [ABSTRACT FROM AUTHOR]

Many chemical reactions and molecular processes occur on time scales that are significantly longer than those accessible by direct simulations. One successful approach to estimating dynamical statistics for such processes is to use many short time series of observations of the system to construct a Markov state model, which approximates the dynamics of the system as memoryless transitions between a set of discrete states. The dynamical Galerkin approximation (DGA) is a closely related framework for estimating dynamical statistics, such as committors and mean first passage times, by approximating solutions to their equations with a projection onto a basis. Because the projected dynamics are generally not memoryless, the Markov approximation can result in significant systematic errors. Inspired by quasi-Markov state models, which employ the generalized master equation to encode memory resulting from the projection, we reformulate DGA to account for memory and analyze its performance on two systems: a two-dimensional triple well and the AIB9 peptide. We demonstrate that our method is robust to the choice of basis and can decrease the time series length required to obtain accurate kinetics by an order of magnitude. [ABSTRACT FROM AUTHOR]

The manipulation of the ultrafast quantum dynamics of a molecular system can be achieved through the application of tailored light fields. This has been done in many ways in the past. In our present investigation, we show that it is possible to exert specific control over the nonadiabatic dynamics of a generic model system describing ultrafast charge-transfer within a condensed dissipative environment by using frequency-chirped pulses. By adjusting the external photoexcitation conditions, such as the chirp parameter, we show that the final population of the excitonic and charge-transfer states can be significantly altered, thereby influencing the elementary steps controlling the transfer process. In addition, we introduce an excitation scheme based on double-pump time-resolved fluorescence spectroscopy using chirped-pulse excitations. Here, our findings reveal that chirped excitations enhance the vibrational system dynamics as evidenced by the simulated spectra, where a substantial signal intensity dependence on the chirp is observed. Our simulations show that chirped pulses are a promising tool for steering the dynamics of the charge-transfer process toward a desired target outcome. [ABSTRACT FROM AUTHOR]

In this study, we extended the optimized potentials for liquid simulation-ionic-liquid virtual site (OPLS-VSIL) force field (FF) to imidazolium-based dicationic ionic liquids (DILs) and evaluated the ability of different OPLS-based FFs (i.e., OPLS-2009IL, 0.8*OPLS-2009IL, and OPLS-VSIL) in predicting different properties of the studied DIL by comparing their results with ab initio molecular dynamics (AIMD) simulation and experimental results. To achieve this purpose, MD simulations with three different OPLS-based FFs as well as AIMD simulation were performed for [C3(mim)2][NTF2]2 DIL and its structural, dynamical, vibrational, and volumetric properties were analyzed. Structural properties of the studied DIL, i.e., radial distribution functions (RDFs), structure factor, and hydrogen-bond network, showed that compared to 0.8*OPLS-2009IL FF, there is a much better agreement between the results of both OPLS-2009IL and OPLS-VSIL FFs with the AIMD simulation. On the other hand, the results of dynamical properties, such as mean square displacements, van Hove correlation functions as well as hydrogen bond, ion pair, and ion cage dynamics, depicted that in both 0.8*OPLS-2009IL and OPLS-VSIL FFs, the dynamics of the system is almost similar, and compared to OPLS-2009IL FF, they have better agreements with experimental results where they exist. So, it can be seen that although reducing the total charge of studied DIL by 20% leads to an increase in the dynamics of the system, the type distribution of partial charges on each atom does not significantly affect the system's dynamics. The calculated infrared (IR) and power spectra showed that the vibrational features of studied DIL in three OPLS-based FFs are mostly the same and reducing total charge and different type distribution of partial charges have no significant effect on the studied system. Furthermore, in volumetric properties, OPLS-VSIL FF shows somehow better agreement with experimental results. Overall, the evaluation of different structural, dynamical, vibrational, and volumetric properties of [C3(mim)2][NTF2]2 DIL shows that the OPLS-VSIL FF may be the best choice among the different studied OPLS FFs. [ABSTRACT FROM AUTHOR]

Modeling the dynamics of glassy systems has been challenging in physics for several decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in capturing particle dynamics from the graph structure of glassy systems. However, current GNN methods do not take the dynamic patterns established by neighboring particles explicitly into account. In contrast to these approaches, this paper introduces a novel dynamical parameter termed "smoothness" based on the theory of graph signal processing, which explores the dynamic patterns from a graph perspective. Present graph-based approaches encode structural features without considering smoothness constraints, leading to a weakened correlation between structure and dynamics, particularly on short timescales. To address this limitation, we propose a Geometry-enhanced Graph Neural Network (Geo-GNN) to learn the smoothness of dynamics. Results demonstrate that our method outperforms state-of-the-art baselines in predicting glassy dynamics. Ablation studies validate the effectiveness of each proposed component in capturing smoothness within dynamics. These findings contribute to a deeper understanding of the interplay between glassy dynamics and static structure. [ABSTRACT FROM AUTHOR]

We present the study of the structure–dynamics correlation for systems interacting via attractive Lennard-Jones (LJ) and its repulsive counterpart, the Weeks–Chandler–Andersen (WCA) potentials. The structural order parameter (SOP) is related to the microscopic mean-field caging potential. At a particle level, the SOP shows a distribution. Although the two systems have similar pair structures, their average SOP differs. However, this difference alone is insufficient to explain the well known slowing down of the dynamics in the LJ system at low temperatures. The slowing down can be explained in terms of a stronger coupling between the SOP and the dynamics. To understand the origin of this system specific coupling, we study the difference in the microscopic structure between the hard and soft particles. We find that for the LJ system, the structural differences of the hard and soft particles are more significant and have a much stronger temperature dependence compared to the WCA system. Thus, the study suggests that attractive interaction creates more structurally different communities. This broader difference in the structural communities is probably responsible for stronger coupling between the structure and dynamics. Thus, the system specific structure–dynamics correlation, which also leads to a faster slowing down in the dynamics, appears to have a structural origin. A comparison of the predictive power of our SOP with the local energy and two body excess entropy in determining the dynamics shows that in the LJ system, the enthalpy plays a dominant role and in the WCA system, the entropy plays a dominant role, and our SOP can capture both these contributions. [ABSTRACT FROM AUTHOR]

We propose a novel UV/Vis femtosecond spectroscopic technique, two-dimensional fluorescence-excitation (2D-FLEX) spectroscopy, which combines spectral resolution during the excitation process with exclusive monitoring of the excited-state system dynamics at high time and frequency resolution. We discuss the experimental feasibility and realizability of 2D-FLEX, develop the necessary theoretical framework, and demonstrate the high information content of this technique by simulating the 2D-FLEX spectra of a model four-level system and the Fenna–Matthews–Olson antenna complex. We show that the evolution of 2D-FLEX spectra with population time directly monitors energy transfer dynamics and can thus yield direct qualitative insight into the investigated system. This makes 2D-FLEX a highly efficient instrument for real-time monitoring of photophysical processes in polyatomic molecules and molecular aggregates. [ABSTRACT FROM AUTHOR]

Simulations of laser-induced electron dynamics in a molecular system are performed using time-dependent (TD) equation-of-motion (EOM) coupled-cluster (CC) theory. The target system has been chosen to highlight potential shortcomings of truncated TD-EOM-CC methods [represented in this work by TD-EOM-CC with single and double excitations (TD-EOM-CCSD)], where unphysical spectroscopic features can emerge. Specifically, we explore driven resonant electronic excitations in magnesium fluoride in the proximity of an avoided crossing. Near the avoided crossing, the CCSD similarity-transformed Hamiltonian is defective, meaning that it has complex eigenvalues, and oscillator strengths may take on negative values. When an external field is applied to drive transitions to states exhibiting these traits, unphysical dynamics are observed. For example, the stationary states that make up the time-dependent state acquire populations that can be negative, exceed one, or even complex-valued. [ABSTRACT FROM AUTHOR]

Ultrafast two-dimensional infrared (2DIR) spectroscopy is a relatively new methodology, which has now been widely used to study the molecular structure and dynamics of molecular processes occurring in solution. Typically, in 2DIR spectroscopy the dynamics of a system is inferred from the evolution of 2DIR spectral features over waiting times. One of the most important metrics derived from the 2DIR is the frequency–frequency correlation function (FFCF), which can be extracted using different methods, including center and nodal line slope. However, these methods struggle to correctly describe the dynamics in 2DIR spectra with multiple and overlapping transitions. Here, a new approach, utilizing pseudo-Zernike moments, is introduced to retrieve the FFCF dynamics of each spectral component from complex 2DIR spectra. The results show that this new method not only produces equivalent results to more established methodologies in simple spectra but also successfully extracts the FFCF dynamics of individual component from very congested and unresolved 2DIR spectra. In addition, this new methodology can be used to locate the individual frequency components from those complex spectra. Overall, a new methodology for analyzing the 2D spectra is presented here, which allows us to retrieve previously unattainable spectral features from the 2DIR spectra. [ABSTRACT FROM AUTHOR]

A simulation of the non-adiabatic dynamics of a quantum system coupled to dissipative environments poses significant challenges. New sophisticated methods are regularly being developed with an eye toward moving to larger systems and more complicated descriptions of solvents. Many of these methods, however, are quite difficult to implement and debug. Furthermore, trying to make the individual algorithms work together through a modular application programming interface can be quite difficult as well. We present a new, open-source software framework, QuantumDynamics.jl, designed to address these challenges. It provides implementations of a variety of perturbative and non-perturbative methods for simulating the dynamics of these systems. Most prominently, QuantumDynamics.jl supports hierarchical equations of motion and methods based on path integrals. An effort has been made to ensure maximum compatibility of the interface between the various methods. Additionally, QuantumDynamics.jl, being built on a high-level programming language, brings a host of modern features to explorations of systems, such as the usage of Jupyter notebooks and high level plotting, the possibility of leveraging high-performance machine learning libraries for further development. Thus, while the built-in methods can be used as end-points in themselves, the package provides an integrated platform for experimentation, exploration, and method development. [ABSTRACT FROM AUTHOR]

The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here, we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To achieve this, we introduce a method to pinpoint the cage state of the initial configuration—i.e., the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime. [ABSTRACT FROM AUTHOR]

In this work, we derive a multi-fragment real-time extension of the projected density matrix embedding theory (pDMET) designed to treat non-equilibrium electron dynamics in strongly correlated systems. As in the previously developed static pDMET, the real time pDMET partitions the total system into many fragments; the coupling between each fragment and the rest of the system is treated through a compact representation of the environment in terms of a quantum bath. The real-time pDMET involves simultaneously propagating the wavefunctions for each separate fragment–bath embedding system along with an auxiliary mean-field wavefunction of the total system. The equations of motion are derived by (i) projecting the time-dependent Schrödinger equation in the fragment and bath space associated with each separate fragment and by (ii) enforcing the pDMET matching conditions between the global 1-particle reduced density matrix (1-RDM) obtained from the fragment calculations and the mean-field 1-RDM at all points in time. The accuracy of the method is benchmarked through comparisons to time-dependent density-matrix renormalization group and time-dependent Hartree–Fock (TDHF) theory; the methods were applied to a one- and two-dimensional single-impurity Anderson model and multi-impurity Anderson models with ordered and disordered distributions of the impurities. The results demonstrate a large improvement over TDHF and rapid convergence to the exact dynamics with an increase in fragment size. Our results demonstrate that the real-time pDMET is a promising and flexible method that balances accuracy and efficiency to simulate the non-equilibrium electron dynamics in heterogeneous systems of large size. [ABSTRACT FROM AUTHOR]

In the field of molecular nonequilibrium transports, physical mechanisms of multiple reaction dynamics of these systems are the core of deep understanding complex reactions and transport mechanisms. In order to explore related mechanisms, establishing multiple systems coupled with tremendous exit dynamics and studying their exit dynamics properties are quite vital. Beyond previous researches, new stochastic transport processes are emphasized here. Multiple new exit dynamic systems are established, which are motivated by the multiplicity of paths and products of real biochemical processes in organisms. In order to ensure research universality, core system modeling factors are fully considered. Countable parallel orbits, uniform connection with external sources, countable parallel orbits as subsystems in middle lattices and influences of all lattices on transport trajectories on dynamic properties are analyzed. Dynamic properties of different particles located in orbits are explored by deeply studying average exit time and time scale. Quantitative spatiotemporal impacts are extensively studied. The rationality of average exit time as a time scale in the universal exit dynamic system is proved. Main findings and fruitful results can not only serve as theoretical bases for broadening reaction path modeling, but also be helpful to support understanding nonequilibrium transport mechanisms, especially stochastic biochemical processes. [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]

Early descriptions of clinical reasoning have described a dual process model that relies on analytical or nonanalytical approaches to develop a working diagnosis. In this classic research, clinical reasoning is portrayed as an individual‐driven cognitive process based on gathering information from the patient encounter, forming mental representations that rely on previous experience and engaging developed patterns to drive working diagnoses and management plans. Indeed, approaches to patient safety, as well as teaching and assessing clinical reasoning focus on the individual clinician, often ignoring the complexity of the system surrounding the diagnostic process. More recent theories and evidence portray clinical reasoning as a dynamic collection of processes that takes place among and between persons across clinical settings. Yet, clinical reasoning, taken as both an individual and a system process, is insufficiently supported by theories of cognition based on individual clinicals and lacks the specificity needed to describe the phenomenology of clinical reasoning. In this review, we reinforce that the modern healthcare ecosystem – with its people, processes and technology – is the context in which health care encounters and clinical reasoning take place. [ABSTRACT FROM AUTHOR]

Fibroblasts significantly affect wound healing, cancer progression and development. Disturbances in the calcium ( Ca 2 +) and dopamine (DA) dynamics leads to fibrotic disorders like cancer and fibrosis. Calcium signaling is required for DA concentrations in fibroblasts. Alteration in the many processes of Ca 2 + kinetics can disturb DA regulation in fibroblast cells. No model has been reported till date for the study of spatiotemporal relationships of DA with calcium signaling in fibroblasts. A model is provided here to study the dynamic relationship of DA regulation with calcium signaling in a fibroblast cell. Finite element and Crank–Nicholson techniques are employed for numerical simulation. The numerical results provide insights into the buffer, source influx, and diffusion effects on the spatiotemporal dynamics of Ca 2 + and DA in a fibroblast. Disturbances in the constitutive processes in the system dynamics of Ca 2 + and DA can cause fibrotic diseases such as fibrosis and cancer in various organs. [ABSTRACT FROM AUTHOR]

An adaptive control law encompasses a regulating control rule compensating for system dynamics variations by adjusting the controller characteristics to maintain the overall system performance. Recently, some techniques have been developed based on fundamental aspects of the adaptability of living organisms. The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between these two to commute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. Here, it has also been investigated that the adaptation gain range is wide for the systems with the lower order. The appropriate range of adaptation gain decreases as the order of the system increases. In the present work, the model reference adaptive control (MRAC) for first- and second-order systems has been designed and investigated using a wide range of values for the adaptation gain and variations in the reference model parameters. The MIT (Massachusetts Institute of Technology) and Lyapunov rules are applied for the analyses of systems. On the MATLAB/Simulink platform, all the adaptation process comparisons, variations, and investigations have been carried out by altering the adaptation gain and the reference model parameters. The obtained results present encouraging outcomes. [ABSTRACT FROM AUTHOR]

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application. [ABSTRACT FROM AUTHOR]

We study the exceptional-point (EP) structure and the associated quantum dynamics in a system consisting of a non-Hermitian qubit and a Hermitian qubit. We find that the system possesses two sets of EPs, which divide the system-parameter space into P T -symmetry unbroken, partially broken and fully broken regimes, each with distinct quantum-dynamics characteristics. Particularly, in the partially broken regime, while the P T -symmetry is generally broken in the whole four-dimensional Hilbert space, it is preserved in a two-dimensional subspace such that the quantum dynamics in the subspace are similar to those in the P T -symmetry unbroken regime. In addition, we reveal that the competition between the inter-qubit coupling and the intra-qubit driving gives rise to a complex pattern in the EP variation with system parameters. [ABSTRACT FROM AUTHOR]

In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm. [ABSTRACT FROM AUTHOR]

This article studies the problem of prescribed‐time constrained tracking control for a class of 2×2$$ 2\times 2 $$ hyperbolic partial differential equation (PDE) systems with actuator dynamics, which are described by a set of nonlinear ordinary differential equations (ODEs). Since the control input only appears in the ODE subsystem rather than directly on the boundary of PDE subsystem, the control task becomes quite challenging. The most important is that for the control of such ODE controlled PDE systems we mainly make the following two contributions: (1) the controlled output of the PDE system tracks the reference signal within the prescribed time; (2) the controlled output and all the actuator states are constrained. It is the first time that such a prescribed‐time constrained tracking control problem is addressed for the PDE‐ODE coupled system considered in this article. Through rigorous theoretical proof, it is demonstrated that all the system states and control signals are bounded and sufficiently continuous by configuring appropriate design parameters. Finally, the performance is investigated via numerical simulation. [ABSTRACT FROM AUTHOR]

From a mathematical viewpoint, the frequency domain analysis of vessel motion responses due to wave actions is based on integration of system dynamics idealized in terms of response amplitude operators for six degrees-of-freedom (DOFs) rigid body motions and an input wave spectrum in order to obtain the response spectrum. Various quantities of interest can be deduced from the response spectrum, which are then used for deriving response-based operational limits for marine operations, also including extreme value and fatigue analysis. The variation of such quantities, owing to the uncertainties associated with the vessel system parameters, can be quantified by performing uncertainty propagation and consequent sensitivity analysis. This study emphasizes and proposes a computational-efficient way of assessing the sensitivity of the system model output with respect to the uncertainties residing in the input parameters by operating on a surrogate model representation. In this respect, the global sensitivity analysis was effectively carried out by deploying an efficient nonintrusive polynomial chaos expansion surrogate model built using a point collocation strategy. Successively, Sobol' indices were obtained from the analytical decomposition of the polynomial coefficients. The indices, eventually, are employed to quantitatively measure the effects of input uncertainties on the output 6DOF vessel root-mean-square responses. [ABSTRACT FROM AUTHOR]

The axle box bearing serves as a critical rotational component for high-speed trains. Thus, it is of great significance to obtain accurate loading conditions for axle box bearings for its reliability design. In this study, a bearing-vehicle coupled system dynamic model, consisting of an equivalent bearing model characterized by the time-varying stiffness in the bearing, a nonlinear high-speed train dynamic model, and a slab track model, is developed. The validity of the proposed coupled dynamic model is demonstrated by comparing the simulated results with those obtained in a field test and the literature, respectively. Subsequently, the influence of long- and short-wavelength irregularities on the dynamic responses of bearings is investigated using the proposed coupled model in terms of dynamic loads of the axle box bearing. The results obtained by the proposed model are further compared with those calculated using a traditional vehicle dynamic model that represents the bearing as a revolute joint, so as to illustrate the advantages of the proposed model. The results suggest that the loading responses of the bearing calculated by the two models exhibit differences, which are simultaneously related to the structural damping of the bearing and the mode frequency change of the system. [ABSTRACT FROM AUTHOR]

Polygonalization is a typical non-uniform wear phenomenon that affects the performance and comfort of the vehicle/track system. Therefore, the vehicle system performance could be strictly troubled by polygonal damage close to the wheel circumference at the wheel/rail contact. A 3D vehicle/track an EMU type train consisting of 8 coupled vehicles model has been established. The coupled model car body and bogie frame are considered rigid and integrated with the flexible wheelset, rail, and slab track to investigate the elastic deformation caused by the impact load induced by the wheel polygonalization. The flexible track can be incorporated into the multi-body vehicle model using the movable reference frame formulation. Uncertainty parameters are presented, such as passenger weight, wind force, coefficient friction, and suspension variation. The proposed research is validated based on the existing research work and shows excellent agreement. The simulation result deliberated the vertical wheel-rail force, wheelset acceleration, rail acceleration, rail displacement, and slab track acceleration. The results of this study show that as the wheel polygonal damage amplitude increase, the value of wheel/rail normal forces and the dynamic vibrational response of the wheelset axle increase. Additionally, polygonal wear-induced impact stresses may cause the wheelset axle to resonate. When wheel polygonal damage amplitude and uncertainty parameters are corporate into the system dynamics, the maximum magnitude of the wheelset, rail, and slab track acceleration increases. Besides, the train failure degree is comparable to the polygonised wheel harmonic order, defect parameters, and amplitude of out-of-roundness. [ABSTRACT FROM AUTHOR]

This editorial paper reviews the articles published in a special issue devoted to the application of active particle methods applied to the study of the collective dynamics of large systems of interacting entities in science and society. The applications presented in this special issue focus on the study of financial markets, cell dynamics in the context of cancer modeling, vehicle and crowd vehicle and crowd dynamics, and classical problems in the kinetic theory of active particles and swarm theory. A critical analysis is proposed to look forward to research. Perspectives with emphasis on the interaction of multiple social dynamics. [ABSTRACT FROM AUTHOR]

The prefabricated building has been widely promoted in recent years as it can effectively alleviate the conflict between economic growth and environmental resources. However, the development of the prefabricated building has fallen short of anticipated goals under the influence of the dynamic circumstances and behavioral strategies of multiple stakeholders. Understanding the relevant stakeholders' behavioral strategies and collaborative evolution mechanisms is key to promoting prefabricated buildings' orderly and efficient development. Therefore, this study combines the evolutionary game theory with system dynamics, introduces Gaussian white noise stochastic disturbance terms to model the complex characteristics of multiagent behavior toward prefabricated buildings, and establishes evolutionary game models and stochastic evolutionary game models for local governments, contractors, and consumers. Subsequently, the influences of strategy choice behavior with or without central government supervision were analyzed to study the collaborative driving mechanism of prefabricated buildings under the multiple effects of the government and market. The findings of this research underscore the necessity for government and market collaboration in championing the sustainable evolution of prefabricated buildings. While central government supervision spurs the growth of these structures, its static reward–punishment approach offers only fleeting collaborative momentum and fails to ensure market steadiness. In contrast, the improved dynamic incentive and disincentive mechanism can effectively control fluctuations in the evolutionary process, which is critical in achieving stable development and collaborative governance toward prefabricated buildings. This study contributes to the body of knowledge by broadening the horizons of evolutionary game theory applications and providing a perspective for understanding the behavioral strategies driving the development of prefabricated buildings by both government and market forces. Therefore, a series of driving mechanisms is proposed, providing theoretical guidance and practical insights to prompt the long-term development of prefabricated buildings more effectively. [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]

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications. [ABSTRACT FROM AUTHOR]

The transport sector is under scrutiny because of its significant greenhouse gas emissions. Essential strategies, particularly the adoption of zero- and low-emission vehicles powered by electricity, are crucial for mitigating emissions in road transport. Pickups, which are integral to Mexico's fleet, contribute to such emissions. Thus, implementing effective policies targeting pickups is vital for reducing air pollution and aligning with Mexico's decarbonization objectives. This paper presents a simulation model based on system dynamics to represent the adoption process of zero- and low-emission vehicles, with a focus on pickups and utilizing data from the Mexican case. Three policy evaluation scenarios are proposed based on the simulation model: business as usual; disincentives for zero- and low-emission vehicles; and incentives for unconventional vehicles. One of the most significant findings from this study is that even in a scenario with a greater number of vehicles in circulation, if the technology is fully electric, the environmental impact in terms of emissions is lower. Additionally, a comprehensive sensitivity analysis spanning a wide spectrum is undertaken through an extensive computational process, yielding multiple policy scenarios. The analysis indicates that to achieve a maximal reduction in the country's emissions, promoting solely hybrid electric vehicles and plug-in hybrid electric vehicles is advisable, whereas internal combustion engines, vehicular natural gas, and battery electric vehicles should be discouraged. [ABSTRACT FROM AUTHOR]

In the context of China's new power system, various regions have implemented policies mandating the integration of new energy sources with energy storage, while also introducing subsidies to alleviate project cost pressures. Currently, there is a lack of subsidy analysis for photovoltaic energy storage integration projects. In order to systematically assess the economic viability of photovoltaic energy storage integration projects after considering energy storage subsidies, this paper reviews relevant policies in the Chinese photovoltaic energy storage market. It analyzes the cost and revenue composition of photovoltaic energy storage integration projects, and constructs a system dynamics model for the levelized cost of electricity (LCOE) of such projects. Taking a specific photovoltaic energy storage project as an example, this paper measures the levelized cost of electricity and the investment return rate under different energy storage scenarios. Combining energy storage allocation ratios and internal rate of return indicators, this paper analyzes the net present value of photovoltaic energy storage integration projects under different subsidy standards. The results indicate that, while the current energy storage subsidy policies positively stimulate photovoltaic energy storage integration projects, they exhibit a limited capacity to cover energy storage investment costs, thereby failing to incentivize capital market participation in the construction of such projects. Rational allocation of energy storage capacity and optimization of corresponding subsidy policies are crucial prerequisites for enhancing the economic viability and widespread adoption of photovoltaic energy storage integration projects. This study not only aids in investment decision making for photovoltaic power stations but also contributes to the formulation of energy storage subsidy policies. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R 2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R 2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831. [ABSTRACT FROM AUTHOR]

The human inclination to value rarity contributes to the exploitation of many wildlife species by driving up their market value when their numbers are scarce. This research investigates a model to explore how the value attached to rarity impacts the harvesting of predator species and subsequently influences the dynamics of predator-prey interactions in an ecological system. To broaden the model's scope, it's extended to its nonautonomous form by allowing the harvesting rate of predator species and the degree of neutrality in consumers to respond for the rarity of predator species to change over time. Both the autonomous and nonautonomous systems are explored through analytical and numerical investigation. Numerical findings reveal that as the prey population's growth rate gradually increases, the unforced system shifts from stable boundary equilibrium (harvesting absent) to stable interior equilibrium, then to oscillatory interior equilibrium, followed by period doubling oscillations leading to chaotic behavior. The autonomous system exhibits limit cycle oscillations around the boundary equilibrium when predator species are minimally harvested. Moderate harvesting stabilizes the system, while excessive harvesting induces instability. However, the chaotic oscillations can be eliminated through period-halving route upon decreasing the consumers' response to predator rarity. Furthermore, the seasonally forced model demonstrates periodic solutions, higher order periodic solutions, and chaotic dynamics contingent upon different settings of prey growth rate, the strength of seasonality in predator harvesting, and the consumers' response to predator rarity. [ABSTRACT FROM AUTHOR]

This article explores the mechanical analysis of a unified chaotic system and matrix projective synchronization (MPS). The sufficient conditions to achieve MPS of unified chaotic system have derived. The mechanics of unified chaotic system have been examined in contrast with Kolmogorov system, Euler equation, and Hamiltonian function. The Casimir energy function is also introduced to analyze the system dynamics. The unified chaotic system has been transformed into Kolmogorov type system and decomposed into four types of torques: inertial, internal, dissipation, and external torque. In order to view the mechanics behind the unified chaotic system, different particular cases have been discussed. Five scenarios are examined using combinations of various torques in order to identify the key elements in chaos creation and their physical significance. The bifurcation diagram, Lyapunov exponent and Lyapunov dimension validates the generation of chaos for different particular cases [ABSTRACT FROM AUTHOR]

This article investigates the policy iteration (PI) method for the discounted optimal control (DOC) problem of continuous‐time linear systems. We show the properties and convergence of the PI method. The theory analysis shows that the convergence of PI can be ensured without requiring the initial admissible control gain. The convergence rate of the PI method is provided. An iteration‐termination criterion is established for detecting the stability of the closed‐loop system under the control gain obtained by executing PI. Two kinds of data‐driven implementations are constructed without using prior information of the system dynamics. A simulation example is presented to validated the properties of the PI method. [ABSTRACT FROM AUTHOR]

This article realizes an adaptive finite‐time sampled‐data output‐feedback stabilization for a class of fractional‐order nonlinear systems with unmodeled dynamics and unavailable states. K‐filters are constructed to estimate unavailable states, a dynamic signal is introduced to handle unmodeled dynamics and neural networks were used to approximate uncertain nonlinearities existed in stabilizer construction. With the help of backstepping technique, an adaptive sampled‐data output‐feedback stabilizer is exported, and such stabilizer with allowable design parameters and sampling period can render the corresponding closed‐loop system reaches practically finite‐time stable, which can be demonstrated by means of selected Lyapunov function candidates. In the end, two simulations with a numerical and an engineering examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

To address the existing shortcomings in the research on the coupling of safety risk factors in subway tunnel construction using the shallow-buried excavation method, this paper conducts a coupled analysis and dynamic simulation of the safety risks associated with this construction method. Firstly, by analyzing the mechanisms and effects of risk coupling in shallow-buried excavation construction of subway tunnels, this study divides the risk system into four risk subsystems (human, material, management, and environment), establishes an evaluation index system for the coupling of safety risks, calculates the comprehensive weight values of the risk indicators using the AHP-entropy weight method, and constructs a risk coupling degree model by combining the inverse cloud model and efficacy function. Subsequently, based on the principles of system dynamics, a causal relationship diagram and a system dynamics simulation model for the coupling of “human-material” risks in construction are established using Vensim PLE software. Finally, the case study of the underground excavation section of Chengdu Metro Line 2 is employed to perform dynamic simulation using the established model. By adjusting the relevant risk coupling coefficients and simulation duration, the impact of the coupling of various risk factors on the safety risk level of the human-material coupling system is observed. The simulation results demonstrate that: 1) Heterogeneous coupling of human and material risks has a particularly significant effect on the system’s safety risks; 2) Violations by personnel and initial support structure defects are key risk coupling factors. The findings of this study provide new insights for decision-makers to assess the safety risk of shallow-buried excavation construction in subway tunnel. [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]

This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the "explosion of complexity" issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller. [ABSTRACT FROM AUTHOR]

We studied the characteristics of a four-channel multi-task reservoir computing (RC) system based on mutually-coupled and optically pumped spin vertical-cavity surface-emitting lasers (Spin-VCSELs). By analyzing bifurcation and state diagrams, we investigated the polarization dynamics of the system. Additionally, we examined the effects of key parameters such as coupling strength, injection strength, frequency detuning, optical pumping intensity, and pump polarization ellipticity on RC performance, with one-step time-series prediction as the benchmark. Our findings indicate that an increase in coupling strength results in a reduced frequency detuning range, accompanied by a lower normalized mean squared error (NMSE). Excessive coupling strength and optical pumping intensity are detrimental to RC performance. The influence of pump polarization ellipticity is smaller and exhibits a symmetry feature. Furthermore, the parameter optimization regime for orthogonal polarized optical coupling (OPOC) is wider than that for parallel polarized optical coupling (PPOC). Under optimized parameters, both PPOC and OPOC-based RC system can achieve NMSE as low as 0.033 and 0.041 respectively. This work can provide a valuable insight on the design and mechanism analysis for parallel and multi-task processing RC systems based on the platform of Spin-VCSELs. [ABSTRACT FROM AUTHOR]

In a renewable-energy-penetrated power system (RPPS), inverter-based resources (IBRs) pose serious challenges to power system stability due to their completely different dynamic characteristics compared with conventional generators; thus, it is necessary to study the dynamic interactions between IBRs and power systems. Although many research efforts have been dedicated to this topic from both power electronics and power system researchers, some research from the power electronics field treats the external power system as a voltage source with an impedance, therefore ignoring the dynamic characteristics of a power system, while most of the research from the power system field applies simulation-based methods, for which it is difficult to directly interpret the interaction mechanism of IBRs and external system dynamics. Thus, none of these studies can explore the accurate dynamic interaction mechanism between IBRs and power systems, leading to performance degradation of IBR-integrated power systems. Our study takes into account the dynamic characteristics of both IBRs and the external power system, resulting in the development of a new open-loop transfer function for RPPSs. Based on this formulation, it is observed that under certain operating conditions, the dynamic interactions between the inverter and the power system help enhance IBR-penetrated power system stability compared with the case for which the external power system is controlled as a voltage source. The study also reveals how the inverter (phase-locked loop, control parameters, etc.), external power system (network strength) and penetration ratio in an IBR-penetrated power system affect the dynamic interactions between IBRs and the external power system using the proposed quantified interaction indices. [ABSTRACT FROM AUTHOR]

This review offers a comprehensive exploration of the intricate immunological landscape of breast cancer (BC), focusing on recent advances in diagnosis and prognosis through the analysis of circulating tumor cells (CTCs). Positioned within the broader context of BC research, it underscores the pivotal role of the immune system in shaping the disease's progression. The primary objective of this investigation is to synthesize current knowledge on the immunological aspects of BC, with a particular emphasis on the diagnostic and prognostic potential offered by CTCs. This review adopts a thorough examination of the relevant literature, incorporating recent breakthroughs in the field. The methodology section succinctly outlines the approach, with a specific focus on CTC analysis and its implications for BC diagnosis and prognosis. Through this review, insights into the dynamic interplay between the immune system and BC are highlighted, with a specific emphasis on the role of CTCs in advancing diagnostic methodologies and refining prognostic assessments. Furthermore, this review presents objective and substantiated results, contributing to a deeper understanding of the immunological complexity in BC. In conclusion, this investigation underscores the significance of exploring the immunological profile of BC patients, providing valuable insights into novel advances in diagnosis and prognosis through the utilization of CTCs. The objective presentation of findings emphasizes the crucial role of the immune system in BC dynamics, thereby opening avenues for enhanced clinical management strategies. [ABSTRACT FROM AUTHOR]

The dynamics and vibration reduction performance of bistable nonlinear energy sink (BNES) are studied in this paper. First, the negative stiffness of BNES is realized by geometric nonlinearity, and the dynamics model of the system is established. The slow flow equation of the system under 1:1 main resonance is analyzed based on the complexification-averaging (CX-A) method, and the boundary conditions of saddle node bifurcation and Hopf bifurcation are analyzed. Second, the slow invariant manifold (SIM) of the BNES is studied based on multiscale analysis, and the excitation threshold of strongly modulated response (SMR) is analyzed; the analysis results are verified by the numerical method. The results show that the analytical solution is highly consistent with the numerical solution, and the error is less than 1%. Finally, the influence of structural parameters on the vibration reduction performance is analyzed and optimized. The vibration reduction performance of BNES and CNES is compared, and the results show that the BNES has better vibration reduction performance in the full frequency band. [ABSTRACT FROM AUTHOR]

In this paper, the fully distributed containment problem is investigated for a class of second-order nonlinear multi-agent systems (MASs) with disturbance and uncertain control coefficient. In the dynamics of the multi-agent systems, the nonlinear term of the multi-agent systems is Lipshitz, the leaders are subject to bounded inputs, and the disturbances are also bounded. But, the Lipshitz constant, the upper bounds of the disturbances and the leaders' control inputs are unknown; thus, they cannot be used for the design of feedback control. To overcome the difficulty from such unknown bounds and uncertain coefficient, a new fully distributed adaptive protocol is proposed by combining the Lyapunov analysis and adaptive control technique, which based only on relative states of neighboring agents without using any global information, such that the states of the followers converge to the convex hull formed by the states of the leaders. Compared with the existing works, one main contribution is that only one adaptive gain is adopted in the fully distributed adaptive protocol to deal with containment problem of second-order nonlinear MASs, where four unknown parameters are involved. Two simulation examples are given to show the performance of the proposed protocol. [ABSTRACT FROM AUTHOR]

This special issue of Cognitive Linguistics reexamines the notions of uniformity and variability within morphological systems from a cognitive linguistic standpoint. It challenges traditional perspectives that regard morphological variability as mere deviations from the norm, suggesting instead that such variability is systematic and shaped by external influences including language acquisition and processing constraints. The contributions in this issue promote a shift from isolated analysis to a holistic view of paradigms, classes, and systems, advocating for a framework where morphological structures are seen as integral to communicative and functional aspects of language. By accounting for the broad adaptive dynamics of language systems, the complex interplay between uniformity and variability is revealed as an inherent aspect of language usage. [ABSTRACT FROM AUTHOR]

Summary: This article aims to design a model‐free adaptive tracking controller for discrete‐time nonlinear systems with unknown dynamics and asymmetric control constraints. First, a new Q‐function structure is designed by introducing the control input into the tracking error of the next moment, in order to eliminate the final tracking error, avoid the steady control, and ignore the discount factor. Second, via system transformation, a general performance index is developed to overcome the challenge caused by asymmetric constraints of implicit control inputs. By this operation, the constrained tracking problem is converted to an unconstrained optimal tracking problem without the traditional nonquadratic performance function that is only applicable to explicit control inputs. Then, a value‐iteration‐based Q‐learning (VIQL) algorithm is derived to seek the optimal Q‐function and the optimal control policy by using offline data rather than the mathematical model. Next, the convergence, monotonicity, and stability properties of VIQL are investigated to demonstrate that the iterative Q‐function sequence can converge to the optimal Q‐function under ideal conditions. To realize the VIQL algorithm, the critic neural network is employed to approximate the Q‐function. Finally, simulation results and comparative experiments are conducted to demonstrate the validity and effectiveness of the present VIQL scheme. [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]