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Understanding the nonadiabatic dynamics of complex systems is a challenging task in computational photochemistry. Herein, we present an efficient and user-friendly quantum mechanics/molecular mechanics (QM/MM) interface to run on-the-fly nonadiabatic dynamics. Currently, this interface consists of an independent set of codes designed for general-purpose use. Herein, we demonstrate the ability and feasibility of the QM/MM interface by integrating it with our long-term developed JADE package. Tailored to handle nonadiabatic processes in various complex systems, especially condensed phases and protein environments, we delve into the theories, implementations, and applications of on-the-fly QM/MM nonadiabatic dynamics. The QM/MM approach is established within the framework of the additive QM/MM scheme, employing electrostatic embedding, link-atom inclusion, and charge-redistribution schemes to treat the QM/MM boundary. Trajectory surface-hopping dynamics are facilitated using the fewest switches algorithm, encompassing classical and quantum treatments for nuclear and electronic motions, respectively. Finally, we report simulations of nonadiabatic dynamics for two typical systems: azomethane in water and the retinal chromophore PSB3 in a protein environment. Our results not only illustrate the power of the QM/MM program but also reveal the important roles of environmental factors in nonadiabatic processes. [ABSTRACT FROM AUTHOR]

The use of semiconductor nanocrystals in scalable quantum technologies requires characterization of the exciton coherence dynamics in an ensemble of electronically isolated crystals in which system–bath interactions are nevertheless strong. In this communication, we identify signatures of Fano-like interference between excitons and photocarriers in the coherent two-dimensional photoluminescence excitation spectral lineshapes of mixed lead-halide perovskite nanocrystals in dilute solution. Specifically, by tuning the femtosecond-pulse spectrum, we show such interference in an intermediate coupling regime, which is evident in the coherent lineshape when simultaneously exciting the exciton and the free-carrier band at higher energy. We conclude that this interference is an intrinsic effect that will be consequential in the quantum dynamics of the system and will thus dictate decoherence dynamics, with consequences in their application in quantum technologies. [ABSTRACT FROM AUTHOR]

In the previous paper [C. W. Kim and I. Franco, J. Chem. Phys. 160, 214111-1–214111-13 (2024)], we developed a theory called MQME-D, which allows us to decompose the overall energy dissipation process in open quantum system dynamics into contributions by individual components of the bath when the subsystem dynamics is governed by a Markovian quantum master equation (MQME). Here, we contrast the predictions of MQME-D against the numerically exact results obtained by combining hierarchical equations of motion (HEOM) with a recently reported protocol for monitoring the statistics of the bath. Overall, MQME-D accurately captures the contributions of specific bath components to the overall dissipation while greatly reducing the computational cost compared to exact computations using HEOM. The computations show that MQME-D exhibits errors originating from its inherent Markov approximation. We demonstrate that its accuracy can be significantly increased by incorporating non-Markovianity by exploiting time scale separations (TSS) in different components of the bath. Our work demonstrates that MQME-D combined with TSS can be reliably used to understand how energy is dissipated in realistic open quantum system dynamics. [ABSTRACT FROM AUTHOR]

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]

With the rise and rapid development of the e-commerce industry, the pressure borne by the supply chain of e-commerce enterprises is increasing, which greatly increases the complexity and uncertainty of inventory management in the e-commerce supply chain. The study integrates system dynamics and collaborative supply chain inventory management to construct a new model. The study first uses the concept of system dynamics to optimize the supply chain inventory management system; then the collaborative supply chain is used to construct a system model by combining the optimized management system. The results show that the collaborative supply chain inventory management model constructed by the study can better accelerate the product turnover rate, improve the overall operational efficiency and market competitiveness of the supply chain, and can provide new support for the development of e-commerce enterprises. [ABSTRACT FROM AUTHOR]

Multimode behavior plays a key role in a wide range of nonlinear optical phenomena. Multiple exciton-polariton modes can attain macroscopic population as observed in time-integrated measurements. Recent theory work has shown that, rather than being simultaneously in many modes, the population may temporally switch between the modes. However, the origin and the dynamics of multimode condensation has not been experimentally addressed. Here, we study the dynamics of exciton-polariton condensation into multiple modes of Gaussian defect microcavities filled with ladder type polymer gain material methyl-substituted ladder-type poly(p-phenylene) MeLPPP. We deploy a second-order cumulant model to simulate the dynamics of the system and find picosecond-timescale switching between condensate modes. By interferometric measurements we reveal the experimental signatures of such mode competition behaviour. Recent theoretical works have predicted lasing/condensation in multimode exciton-polaritons, hybrid light-matter quasiparticles, but the origin and the dynamics of multimode condensation has not been experimentally investigated. The authors study the dynamics of multimode exciton-polariton condensation, revealing the experimental signatures of mode competition. [ABSTRACT FROM AUTHOR]

In this foreword to the Special Issue "Nonlinear dynamics and game-theoretical modeling in economics and finance" we review the contributions in the issue highlighting the economic results and the connections with aspects of dynamic analysis. Indeed, the common theme of the contributions is the focus on system dynamics and the latest analytical techniques. This issue is devoted to celebrating the 70th birthday of Professor Laura Gardini, who inspired a generation of scholars in the study of discrete-time systems and global methods of analysis. [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]

In this paper, the impact of the strong Allee effect and ratio-dependent Holling–Tanner functional response on the dynamical behaviors of a predator–prey system is investigated. First, the positivity and boundedness of solutions of the system are proved. Then, stability and bifurcation analysis on equilibria is provided, with explicit conditions obtained for Hopf bifurcation. Moreover, global dynamics of the system is discussed. In particular, the degenerate singular point at the origin is proved to be globally asymptotically stable under various conditions. Further, a detailed bifurcation analysis is presented to show that the system undergoes a codimension- 1 Hopf bifurcation and a codimension- 2 cusp Bogdanov–Takens bifurcation. Simulations are given to illustrate the theoretical predictions. The results obtained in this paper indicate that the strong Allee effect and proportional dependence coefficient have significant impact on the fundamental change of predator–prey dynamics and the species persistence. [ABSTRACT FROM AUTHOR]

The impact of power-law memory on the dynamics of the Caputo standard fractional map is investigated in this paper. The definition of a complexity measure for the Caputo standard fractional map is introduced. This measure evaluates both the average algebraic complexity of a trajectory and the distribution of the trajectories of different types in the phase space of the system. The interplay between the small-scale spatial chaos and the large-scale spatial behavior is observed and measured during the transition of the Caputo standard fractional map from the circle map to the classical standard map. It is demonstrated that the impact of the fractional derivative on the complexity of the fractional system is not straightforward and is predetermined by the physical properties governing the dynamics of that system. [ABSTRACT FROM AUTHOR]

Modeling the nonlinear interactions between flow, sediment, and vegetation is essential for improving our understanding and prediction of river system dynamics. Using simple numerical models, we simulate the key flow‐sediment‐vegetation interaction where the disturbance is intrinsically generated by the presence of vegetation. In this case, biomass growth modifies the flow field, induces bed scour, and thus potentially causes vegetation uprooting when erosion exceeds root depth. Our results show that this nonlinear feedback produces deterministic chaos under a wide range of conditions, with complex aperiodic dynamics generated by a period‐doubling route to chaos. Moreover, our results suggest relatively small values of Lyapunov time, spanning 2–4 growth‐flood cycles, which significantly restrict the predictability of riverbed evolution. Although further spatial and temporal processes may add complexity to the system, these results call for the use of ensemble methods and associated uncertainty estimates in ecomorphodynamic models. Plain Language Summary: Simple models that mimic the interplay between river dynamics and vegetation show that changes in the shape of riverbeds can be unpredictable. Our research suggests that we can make skillful predictions on time scales of the order of a few decades for many typical rivers, but we cannot accurately predict what riverbeds will look like in the long term. This has important implications for how we manage and restore rivers. It means that we need to run multiple numerical simulations to generate different scenarios, similar to the methods used in weather forecasting, to more accurately represent the uncertainty in our predictions. The chaotic behavior we found is not unique to rivers; it may be important in other systems where the interaction of flow, bed, and vegetation is key to how they change over time. Key Points: Coevolution of riverbed and vegetation, mediated by intrinisic biogeomorphic feedbacks, exhibits nonlinear dynamics and chaosFor chaos to be possible, the erosion during a flood must be comparable to the root depthThe time scale on which the riverbed/vegetation evolution becomes unpredictable is short, ranging between 2 and 4 growth‐flood periods [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]

Long-term ecological monitoring is crucial to understanding the complex dynamics of ecosystems, communities, and populations. Despite this, monitoring data are lacking or rare for the vast majority of biodiversity. Here we report the results of 19 years (2003–2022) of continuous annual monitoring of reptile species at Booderee National Park (BNP) on the east coast of south-eastern Australia. We tested the effects of time, habitat type, fire, and climate on detections of five reptile species. Our study revealed declines in detections of two skink species over time (Lampropholis delicata and Ctenotus taeniolatus), which we suspect was partly driven by weather conditions influencing activity of these species. We also identified broad vegetation type associations for two congeneric species with L. delicata being associated with forested sites, and Lampropholis guichenoti associated with more shrubby sites. Our results also demonstrated a clear association between Cryptophis nigrescens and L. delicata and fire, with the probabilities of detection of both species decreasing with time since fire in the short term. At about the midway point of our study (in 2011), we were forced to make a change in the way our data were collected. The change heavily influenced our findings, and so breached the integrity of the time series in our dataset. We acknowledge that a simple but crucial step to mitigate this breach would have been to conduct calibration that allowed subsequent analysis to control for a change in field survey methodology. Whilst improvements in the effectiveness of field survey methods might be possible through new technologies, it is crucial to maintain the integrity of long-term datasets as data collection continues. [ABSTRACT FROM AUTHOR]

In the context of the evolving landscape of reduction in carbon emissions and integration of renewable energy, this study uses system dynamics (SD) modeling to explore the interconnected dynamics of carbon trading (CT), tradable green certificate (TGC) trading, and electricity markets. Using differential equations with time delays, the study provides a comprehensive analysis of structural relationships and feedback mechanisms within and between these markets. Key findings reveal the intricate interplay between carbon prices, green certificate prices, and electricity prices under various coupling mechanisms. For example, under the three-market coupling mechanism, carbon trading prices stabilize around 150 Yuan/ton, while green certificate prices reach a peak of 0.45 Yuan/KWH, impacting electricity prices, which fluctuate between 0.33 and 1.09 Yuan / KWH during the simulation period. These quantitative results shed light on nuanced fluctuations in market prices and the dynamics of anticipated purchases and sales volumes within each market. The insights gleaned from this study offer valuable implications for policy makers and market stakeholders in navigating the complexities of carbon emission reduction strategies, the integration of renewable energy and market equilibrium. By understanding the dynamics of multi-market coupling, stakeholders can better formulate policies and strategies to achieve sustainable energy transitions and mitigate impacts of climate change. [ABSTRACT FROM AUTHOR]

Understanding what regulates ecosystem functional responses to disturbance is essential in this era of global change. However, many pioneering and still influential disturbance‐related theorie proposed by ecosystem ecologists were developed prior to rapid global change, and before tools and metrics were available to test them. In light of new knowledge and conceptual advances across biological disciplines, we present four disturbance ecology concepts that are particularly relevant to ecosystem ecologists new to the field: (a) the directionality of ecosystem functional response to disturbance; (b) functional thresholds; (c) disturbance–succession interactions; and (d) diversity‐functional stability relationships. We discuss how knowledge, theory, and terminology developed by several biological disciplines, when integrated, can enhance how ecosystem ecologists analyze and interpret functional responses to disturbance. For example, when interpreting thresholds and disturbance–succession interactions, ecosystem ecologists should consider concurrent biotic regime change, non‐linearity, and multiple response pathways, typically the theoretical and analytical domain of population and community ecologists. Similarly, the interpretation of ecosystem functional responses to disturbance requires analytical approaches that recognize disturbance can promote, inhibit, or fundamentally change ecosystem functions. We suggest that truly integrative approaches and knowledge are essential to advancing ecosystem functional responses to disturbance. [ABSTRACT FROM AUTHOR]

Global environmental change has redistributed earth's biomass and the inputs and dynamics of basal detrital resources in ecosystems, contributing to the decline of biodiversity. Yet efforts to manage detrital necromass for biodiversity conservation are often overlooked or consider only singular resource types for focal species groups. We argue there is a significant opportunity to broaden our perspective of the spatiotemporal complexity among multiple necromass types for innovative biodiversity conservation. Here, we introduce an ecosystem-scale perspective to disentangling the spatial and temporal characteristics of multiple and distinct forms of necromass and their associated biota. We show that terrestrial and aquatic ecosystems contain a diversity of necromass types, each with contrasting temporal frequencies and magnitudes, and spatial density and configurations. By conceptualising an ecosystem in this way, we demonstrate that specific necromass dynamics can be identified and targeted for management that benefits the unique spatiotemporal requirements of dependent decomposer organisms and their critical role in ecosystem biomass conversion and nutrient recycling. We encourage conservation practitioners to think about necromass quantity, timing of inputs, spatial dynamics, and to engage with researchers to deepen our knowledge of how necromass might be manipulated to exploit the distinct attributes of different necromass types to help meet biodiversity conservation goals. [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 paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration. [ABSTRACT FROM AUTHOR]

In this study, we analyze the CSMA Non-Persistent protocol with a finite number of nodes, providing more accurate results for applications like wireless sensor networks. The finite model addresses scenarios where the node count is moderate, capturing realistic system dynamics. Our analysis reveals a dependency on the node count, impacting system throughput. As the node count increases, throughput behavior aligns with Kleinrock's infinite model. We derive a complex closed-form throughput expression for a finite quantity of nodes in the system, solved numerically, and offer an approximate expression for specific conditions. These insights advance understanding of low-contention network performance, especially in scenarios where the infinite model becomes inadequate. [ABSTRACT FROM AUTHOR]

This research explores the application of metaheuristic algorithms to refine parameter estimation in dynamic systems, with a focus on the inverted pendulum model. Three optimization techniques, Particle Swarm Optimization (PSO), Continuous Genetic Algorithm (CGA), and Salp Swarm Algorithm (SSA), are introduced to solve this problem. Through a thorough statistical evaluation, the optimal performance of each technique within the dynamic methodology is determined. Furthermore, the efficacy of these algorithms is demonstrated through experimental validation on a real prototype, providing practical insights into their performance. The outcomes of this study contribute to the advancement of control strategies by integrating precisely estimated physical parameters into various control algorithms, including PID controllers, fuzzy logic controllers, and model predictive controllers. Each algorithm ran 1000 times, and the SSA algorithm achieved the best performance, with the most accurate parameter estimation with a minimum error of 0.01501 N m and a mean solution error of 0.01506 N m. This precision was further underscored by its lowest standard deviation in RMSE (1.443 99 × 10−6 N m), indicating remarkable consistency across evaluations. The 95% confidence interval for error corroborated the algorithm's reliability in deriving optimal solutions. [ABSTRACT FROM AUTHOR]

Distributed Energies Resources (DERs) can be controlled for supporting the voltage regulation at nodes of an Active Distribution Network (ADN) where they are connected. However, since the ADN is a Multi-Input Multi-Output (MIMO) system with coupled dynamics, the controller of a DER mutually interacts with all other controllers through the distribution lines. These interactions lead to operating conflicts which may drive the ADN to work close to its voltage stability boundaries. To achieve a stable voltage regulation without new investment in the existing ADNs, the present paper proposes a straightforward decentralized design of the multi-loop controllers based on the property of integral controllability. The main feature of the method is that the design problem can be expressed by a single parameter designed both for reducing the effects of the undesired coupling and for increasing the degree of robust stability in the presence of parameter uncertainty in the matrix plant. Simulation studies are developed to illustrate the design result and the performance achieved under different operating conditions. The performance is also compared with the one obtained by another method in terms of the integral absolute error. [ABSTRACT FROM AUTHOR]

Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region's overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB. [ABSTRACT FROM AUTHOR]

The satellite bears complex vibration loads transmitted from the satellite-rocket interface during launch, where medium–high frequency and small amplitude vibration is highly likely to harm the satellite's internal precision instruments. Under such special conditions, the time-delay factor of the MR damper as the actuator cannot be ignored in the vibration control of the magnetorheological (MR) whole-spacecraft. To compensate actuator time-delay, and consider the system's nonlinear characteristics under medium–high frequency and small amplitude conditions, a gray prediction time-delay compensation method is proposed based on the human-simulated intelligent controller (HSIC). After verifying through simulation and experiment that actuator time-delay affects the vibration response of the MR whole-spacecraft, the HSIC based on gray prediction is designed. Then, the control effect of HSIC with or without time-delay is simulated numerically. The results reveal that after considering the time-delay factor, the resonance peak increases by 17%, and the transmissibility of the concerned particularly frequency band (40 Hz) increases by 62%, which implies a significant deterioration in the control effect. Following, to make up for the response deterioration caused by the time-delay, the HSIC based on gray prediction is used for numerical simulation. The results confirm that reasonable time-delay compensation can effectively improve the system dynamics. This study provides a reference for the practical application of the whole-spacecraft controller. [ABSTRACT FROM AUTHOR]

Intraspecific variation plays a pivotal role in shaping ecological dynamics. As the dispersal of seeds of most woody plants is mediated by animals, individual variation within the animal dispersers holds considerable implications for plant population and ecology.We explored how individual traits (such as sex, body mass and exploration levels) of yellow‐necked mice (Apodemus flavicollis) impact the dispersal of common oak (Quercus robur) acorns. Over 3 years, we collected data on seed fate and the specific mice responsible for their dispersal.The relationship between individual traits and seed dispersal was not static, but influenced by yearly environmental conditions. Heavier individuals tended to carry seeds farther, yet contrary to our expectations, sex had no effect on the distance of seed dispersal. Moreover, the exploration rate showed an inconsistent association with seed consumption and dispersal distance, while it positively impacted the distance of dispersal from the nearest tree.Synthesis. Our findings suggest a more nuanced role of individual traits in seed dispersal than often assumed, with noticeable annual variation significantly influencing these impacts. Consequently, it appears there is no single, universally beneficial individual type to ensure maximal benefits to plants. Rather, the traits conferring advantages in seed dispersal are dynamic, subject to change over time in response to environmental context. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The nonlinear system control is a classical problem in control engineering. In this paper, rather than try to get accurate nonlinear dynamics, the nonlinear and uncertain dynamics are viewed as a signal. It can be estimated by an extended state observer, and compensated by a control law. Accordingly, the nonlinear uncertain system is linearized. Based on the linearized system and the key point of the U-model control, a controller can be designed to obtain predetermined closed-loop system dynamics. To get a more satisfactory performance, a compensation signal of the total disturbance estimation (CSTDE) is designed. Based on the CSTDE, a compensation of the total disturbance estimation based extended state observer (CTDESO) and a fast-response active disturbance rejection control (FRADRC) are proposed. Convergence of the CTDESO and the closed-loop stability of the FRADRC are analyzed. Four nonlinear systems are considered to testify the proposed approaches. Numerical results show that, no matter disturbances exist or not, the proposed CTDESO can linearize a nonlinear system better, and the predetermined closed-loop responses can also be achieved more satisfactorily by the FRADRC. [ABSTRACT FROM AUTHOR]

Economic sanctions have the potential to affect environmental factors through various channels, including income reduction and constraints on accessing finance and environmentally friendly technologies. This study aims to simulate the energy and environmental consequences resulting from international sanctions in Iran. To achieve this, we develop a System Dynamics model integrating a spectrum of variables spanning economic, social, and environmental dimensions, for the period from 2008 to 2028. Our simulation outcomes indicate that by 2028, sanctions are anticipated to precipitate a notable upsurge in CO2 emissions, ranging from 12.5% to 30% compared to the baseline scenario. The magnitude of this escalation hinges on a variety of factors, including energy intensity and scenarios related to economic growth. According to our findings, prolonging international sanctions could be a major barrier to improving energy intensity and lowering CO2 emissions. Given the potential environmental consequences of sanctions, international communities, particularly sanctioning countries, should consider the environmental impacts of sanctions in their policy-making decisions in order to reduce emissions damage. [ABSTRACT FROM AUTHOR]

How policymakers and academics organize and visualize core ideas affects how they define and perceive problems and generate policy solutions. While understanding complex ideas—such as productivity—as the product of a set of discrete inputs can help target inquiry and structure policy interventions, this can also lead to siloed thinking that neglects dynamic effects and interactions between elements. This paper explores how three organizations conceptualize the 'productivity puzzle' and suggests that they might be oversimplifying the roots of productivity. We present a systematic review of labour productivity literature using bibliometric coupling and network analysis to develop an alternative framework and map of themes and gaps. This work encourages policy to (1) adopt a systems lens and perceive productivity as the product of dynamic interactions and (2) think critically about how to structure future research on productivity. [ABSTRACT FROM AUTHOR]

Landscapes of fear can determine the dynamics of entire ecosystems. In response to perceived predation risk, prey can show physiological, behavioral, or morphological trait changes to avoid predation. This in turn can indirectly affect other species by modifying species interactions (e.g., altered feeding), with knock‐on effects, such as trophic cascades, on the wider ecosystem. While such indirect effects stemming from the fear of predation have received extensive attention for herbivore–plant and predator–prey interactions, much less is known about how they alter parasite–host interactions and wildlife diseases. In this synthesis, we present a conceptual framework for how predation risk—as perceived by organisms that serve as hosts—can affect parasite–host interactions, with implications for infectious disease dynamics. By basing our approach on recent conceptual advances with respect to predation risk effects, we aim to expand this general framework to include parasite–host interactions and diseases. We further identify pathways through which parasite–host interactions can be affected, for example, through altered parasite avoidance behavior or tolerance of hosts to infections, and discuss the wider relevance of predation risk for parasite and host populations, including heuristic projections to population‐level dynamics. Finally, we highlight the current unknowns, specifically the quantitative links from individual‐level processes to population dynamics and community structure, and emphasize approaches to address these knowledge gaps. [ABSTRACT FROM AUTHOR]

An increasing number of studies of above‐belowground interactions provide a fundamental basis for our understanding of the coexistence between plant and soil communities. However, we lack empirical evidence to understand the directionality of drivers of plant and soil communities under natural conditions: 'Are soil microorganisms driving plant community functioning or do they adapt to the plant community?' In a field experiment in an early successional dune ecosystem, we manipulated soil communities by adding living (i.e., natural microbial communities) and sterile soil inocula, originating from natural ecosystems, and examined the annual responses of soil and plant communities. The experimental manipulations had a persistent effect on the soil microbial community with divergent impacts for living and sterile soil inocula. The plant community was also affected by soil inoculation, but there was no difference between the impacts of living and sterile inocula. We also observed an increasing convergence of plant and soil microbial composition over time. Our results show that alterations in soil abiotic and biotic conditions have long‐term effects on the composition of both plant and soil microbial communities. Importantly, our study provides direct evidence that soil microorganisms are not "drivers" of plant community dynamics. We found that soil fungi and bacteria manifest different community assemblies in response to treatments. Soil fungi act as "passengers," that is, soil microorganisms reflect plant community dynamics but do not alter it, whereas soil bacteria are neither "drivers" nor "passengers" of plant community dynamics in early successional ecosystems. These results are critical for understanding the community assembly of plant and soil microbial communities under natural conditions and are directly relevant for ecosystem management and restoration. [ABSTRACT FROM AUTHOR]