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Understanding the dynamics of open quantum systems in strong coupling and non-Markovian regimes remains a formidable theoretical challenge. One popular and well-established method of approximation in these circumstances is provided by the polaron master equation (PME). In this work, we re-evaluate and extend the validity of the PME to capture the impact of non-Markovian polaron dressing, induced by non-equilibrium open system dynamics. By comparing with numerically exact techniques, we confirm that while the standard PME successfully predicts the dynamics of system observables that commute with the polaron transformation (e.g., populations in the Pauli z-basis), it can struggle to fully capture those that do not (e.g., coherences). This limitation stems from the mixing of system and environment degrees of freedom inherent to the polaron transformation, which affects the accuracy of calculated expectation values within the polaron frame. Employing the Nakajima–Zwanzig projection operator formalism, we introduce correction terms that provide an accurate description of observables that do not commute with the transformation. We demonstrate the significance of the correction terms in two cases, the canonical spin-boson model and a dissipative time-dependent Landau–Zener protocol, where they are shown to impact the system dynamics on both short and long timescales. [ABSTRACT FROM AUTHOR]

We present a multiscale method coupling the theory of open quantum systems with real-time ab initio treatment of electronic structure to study hot-carrier dynamics in photoexcited plasmonic systems. We combine the Markovian Stochastic Schrödinger equation with an ab initio GW coupled to the Bethe–Salpeter (BSE) equation description of the electronic degrees of freedom, interacting with a metallic nanoparticle modeled classically according to the polarizable continuum model. We apply this methodology to study the effect of relaxation (T1) and pure dephasing (T2) times on the hot-carrier dynamics in a system composed of a quantum portion described at GW/BSE level, i.e., a CHO fragment adsorbed on a vertex of a rhodium nanocube, and of the rest of the nanocube, treated classically, when irradiated with a 2.7 eV light pulse, inspired by the experimental results on plasmon-driven CO2 photoreduction. A net hole injection from rhodium to CHO is observed, with and without the classical portion of the nanocube. The nanocube effect is to enhance the generated charge population by two orders of magnitude. The nonradiative decay, via a relaxation time T1 based on the energy-gap law, produces a rapid decrease of the charge population. Results with T2 only show that a charge injection retarded with respect to the pulse, which is present in the coherent dynamics, disappears when coherence is erased. [ABSTRACT FROM AUTHOR]

EU's Carbon Border Adjustment Mechanism (CBAM) has been promulgated. The steel industry is included in CBAM, which will undoubtedly affect the export trade of China's steel manufacturers to the EU. It is necessary to analyse the impact of CBAM on China's steel industry quantitatively. By establishing a system dynamics model that can simulate the operation mechanism of the steel supply chain in China, we have clarified the impacts of CBAM on the China's steel export and carbon reduction. Moreover, we have reasonably set up scenarios that represent the possible decarbonisation paths of China's steel industry and evaluated the implementation effect of each policy in these scenarios. The analysis shows that CBAM will lead to a decrease of 32% in export volume and a loss of 58% in export profits for China's steel exporters. The environmental benefits of CBAM are limited. Due to the low trade exposure, CBAM is unable to promote a large-scale carbon reduction in the China's steel industry. Although CBAM will bring great pressure to steel exporters, a comprehensive policy of adopting more emission reduction measures can greatly improve the carbon reduction efficiency, effectively reduce the negative impact of CBAM, and maintain the stability of steel exports. [ABSTRACT FROM AUTHOR]

The interaction between cavity photons and molecular vibrations leads to the formation of vibrational polaritons, which have demonstrated the ability to influence chemical reactivity and change material characteristics. Although ultrafast spectroscopy has been extensively applied to study vibrational polaritons, the nonlinear relationship between signal and quantum state population complicates the analysis of their kinetics. Here, we employ a second-order kinetic model and transform matrix method (TMM) to develop an effective model to capture the nonlinear relationship between the two-dimensional IR (or pump–probe) signal and excited state populations. We test this method on two types of kinetics: a sequential relaxation from the second to the first excited states of dark modes, and a Raman state relaxing into the first excited state. By globally fitting the simulated data, we demonstrate accurate extraction of relaxation rates and the ability to identify intermediate species by comparing the species spectra with theoretical ground truth, validating our method. This study demonstrates the efficacy of a second-order TMM approximation in capturing essential spectral features with up to 10% excited state population, simplifying global analysis and enabling straightforward extraction of kinetic parameters, thus empowering our methodology in understanding excited-state dynamics in polariton systems. [ABSTRACT FROM AUTHOR]

Generic open quantum systems are notoriously difficult to simulate unless one looks at specific regimes. In contrast, classical dissipative systems can often be effectively described by stochastic processes, which are generally less computationally expensive. Here, we use the paradigmatic case of a dissipative quantum oscillator to give a pedagogic introduction to the modeling of open quantum systems using quasiclassical methods, i.e., classical stochastic methods that use a "quantum" noise spectrum to capture the influence of the environment on the system. Such quasiclassical methods have the potential to offer insights into the impact of the quantum nature of the environment on the dynamics of the system of interest while still being computationally tractable. [ABSTRACT FROM AUTHOR]

This article introduces an advanced Koopman mode decomposition (KMD) technique—coined Featurized Koopman Mode Decomposition (FKMD)—that uses delay embedding and a learned Mahalanobis distance to enhance analysis and prediction of high-dimensional dynamical systems. The delay embedding expands the observation space to better capture underlying manifold structures, while the Mahalanobis distance adjusts observations based on the system's dynamics. This aids in featurizing KMD in cases where good features are not a priori known. We show that FKMD improves predictions for a high-dimensional linear oscillator, a high-dimensional Lorenz attractor that is partially observed, and a cell signaling problem from cancer research. [ABSTRACT FROM AUTHOR]

Strong coupling between the molecular system and photon inside the cavity generates polaritons, which can alter reaction rates by orders of magnitude. In this work, we benchmark the surface hopping method to simulate non-adiabatic dynamics in a cavity. The comparison is made against a numerically exact method (the hierarchical equations of motion) for a model system investigating excitonic energy transfer for a broad range of parameters. Surface hopping captures the effects of the radiation mode well, both at resonance and off-resonance. We have further investigated parameters that can increase or decrease the rate of population transfer, and we find that surface hopping in general can capture both effects well. Finally, we show that the dipole self-energy term within our parameter regime does not significantly affect the system's dynamics. [ABSTRACT FROM AUTHOR]

In this work, we present a mixed quantum–classical open quantum system dynamics method for studying rate modifications of ground-state chemical reactions in an optical cavity under vibrational strong-coupling conditions. In this approach, the cavity radiation mode is treated classically with a mean-field nuclear force averaging over the remaining degrees of freedom, both within the system and the environment, which are handled quantum mechanically within the hierarchical equations of motion framework. Using this approach, we conduct a comparative analysis by juxtaposing the mixed quantum–classical results with fully quantum-mechanical simulations. After eliminating spurious peaks that can occur when not using the rigorous definition of the rate constant, we confirm the crucial role of the quantum nature of the cavity radiation mode in reproducing the resonant peak observed in the cavity frequency-dependent rate profile. In other words, it appears necessary to explicitly consider the quantized photonic states in studying reactivity modification in vibrational polariton chemistry (at least for the model systems studied in this work), as these phenomena stem from cavity-induced reaction pathways involving resonant energy exchanges between photons and molecular vibrational transitions. [ABSTRACT FROM AUTHOR]

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]

Disabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company. [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 effective operations of closed-loop supply chains (CLSCs) can help companies achieve sustainability goals and boost economic performance. In practice, CLSCs must collect used products, a complex process that is often constrained by collection station capacity. However, how collection station capacity influences the bullwhip effect and the dynamic performance of CLSCs remains unclear. Here, we develop a system dynamics model for CLSCs that integrates traditional manufacturing with remanufacturing and explore the effects of the stochastic capacity constraint of the collection station on the bullwhip effect of CLSCs. We find that, generally speaking, a collection station with looser or more stable capacity constraints tends to reduce bullwhip of CLSCs. However, pertinent interactions emerge between the relevant parameters; in some situations, reducing the capacity level of a collection station may be reasonable and beneficial when the stochastic capacity constraint is very stable, or when customer demand is highly variable. We also consider the partial backlog in return collection, a phenomenon associated with the stochastic capacity constraint of a collection station, and identify a new trade-off between CLSC sustainability and economic performance. Ultimately, our findings provide evidence that will guide managers' plans for the capacity management of return collection in CLSCs. [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]

The rapid growth of the global economy has boosted the shipping industry, but it has also increased energy consumption and exhaust emissions. Based on the system dynamics method, we construct a carbon abatement model of maritime supply chain, in which the interaction among company, economy, energy, environment, and policy is deeply analysed. The effect of various carbon abatement measures is simulated with a case study of Shanghai Port. The results prove that the shore power has tremendous potential for reducing emissions. Interestingly, the emission trading scheme has little effect on emission reduction. Conversely, it will increase CO2 emissions because of the vicious circle of higher CO2 production and decreased CO2 treatment capability. Case 4 is the best alternative for reducing CO2 emissions in maritime supply chain when combined measures are applied, regardless of the emission reduction effect or carbon emission intensity. Thus, it is most effective to mobilise all kinds of resources to reduce CO2 emissions when conditions permit. [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]

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system. [ABSTRACT FROM AUTHOR]

Background: Sustainable supply chain management encompasses the strategic coordination and control of material, information, and financial flows, as well as the collaborative efforts among the entities engaged in the medicinal supply chain. This research proposes a dynamic and sustainable supply chain management model tailored explicitly for the inpatient pharmacies of Medical Centers and Hospitals affiliated with Iran University of Medical Sciences. Methods: This is a quantitative study in terms of research objective and a qualitative study based on the stages in the conceptual development of the model. Therefore, the current study can be considered a mixed-methods approach. After identifying the key factors influencing the sustainability of the medicine supply chain, we conducted a dynamic analysis of the problem using system dynamics methodology. In order to simulate the system's behavior over 24 months, we utilized a combination of existing documentary information and expert opinions. The developed model was implemented using Vensim PLE software, allowing us to simulate and analyze the impact of various policies on the system. Results: Medicine disposal exhibited an upward trend, particularly during the second 12-month period. Conversely, the trend of medicine expirations remained relatively stable in the initial months but showed an upward trajectory after that. The cost associated with disposed medicine experienced a consistent increase, with a higher rate observed during the second 12-month period. In contrast, sales of low-consumable medicine experienced a significant initial surge followed by a slower growth rate. Finally, the pharmacy's profit demonstrated an overall increasing trend, although the rate of increase was higher during the first 12 months. Conclusion: Among the various scenarios considered, namely "increasing the adequacy of human resources," "increasing the speed of response," and "utilizing pharmacists in the drug prescribing team," it was found that these interventions had a substantial effect on both enhancing the pharmacy's profit and reducing medication waste. Therefore, these scenarios were identified as having the most significant impact. The proposed model can serve as a valuable tool for forecasting and informing policy-making, providing insights into addressing the challenges associated with the sustainable drug supply chain in hospital pharmacies. [ABSTRACT FROM AUTHOR]

Water resource carrying capacity is a critical measure of the ability of water resources within a region to support social, ecological, and economic scales. This study establishes an evaluation index system for the water resource carrying capacity of Golmud City and calculates the weights of the indices using the Analytic Hierarchy Process (AHP). Based on an analysis of the current status of Golmud’s socio-economic development, agriculture, and water resource utilization, a simulation model based on system dynamics is constructed. By setting 162 different scenario plans and normalizing the simulation results, the development trend of Golmud City’s water resource carrying capacity is studied, and an evaluation is made for the sustainable development of water resources in Golmud City. Using data from 2001 to 2018 as the basic data, a carrying capacity analysis of water resources for the future from 2019 to 2030 is conducted. The study reveals that continuing the current development pattern will lead to unsustainable water resource carrying capacity. Among the scenarios aimed at coordinated development in all aspects, the best water resource carrying index is achieved, reaching 0.698 by 2030. By analyzing the development trend of the water resource carrying capacity index, existing problems in the utilization of water resources in Golmud City are identified, and feasible strategies for the sustainable use of water resources in Golmud City are proposed. [ABSTRACT FROM AUTHOR]

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra- time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis. [ABSTRACT FROM AUTHOR]

Microelectronics integrity is a critical issue for many industries including the Department of Defense (DoD). The military systems the DoD operates are particularly vulnerable to counterfeiting, with potentially costly or even catastrophic consequences. Counterfeits, regardless of production intent (malign or ersatz), raise significant concerns for industry and the DoD because they often demonstrate operational performance shortcomings, have lower reliability, or make components or organizations more vulnerable to attack. This article uses a systems dynamics modeling approach to explore the economics of counterfeiting for a sample system, the interactions between counterfeiters and the US Navy supply chain, and the impacts of counterfeit surveillance and detection to address the question: Is it more effective to target detection efforts at the component level or at the Line Replaceable Units level? A case study of an engine control module for the LM2500 propulsion turbine used on US Navy Arleigh Burke class guided missile destroyer (DDG‐51) platforms is provided to demonstrate the approach. [ABSTRACT FROM AUTHOR]

In this article, the multiplayer hierarchical decision‐making problem for discrete‐time nonlinear networks of service is studied from the perspective of Nash–Stackelberg–Nash games. A novel two‐level value iteration adaptive dynamic programming algorithm is developed to solve the coupled nonlinear Hamilton–Jacobi–Bellman equations associated with the game problem, which neither requires the system drift dynamics to be known as a priori nor requests the initial strategies to be admissible. Moreover, both the value function sequences and the strategy sequences generated by the algorithm converge to their theoretical optimality. An implementation framework for the algorithm is constructed by leveraging the regularized least‐squares method, and a convergence criterion that guarantees the admissibility of the approximated strategies is also proposed. The effectiveness of the proposed algorithm and implementation framework are finally demonstrated through two numerical simulation examples. [ABSTRACT FROM AUTHOR]

Wood as a renewable material is relevant for climate change mitigation: Carbon sequestration in forests and carbon storage in harvested wood products (HWPs) contribute to carbon reduction in the atmosphere, and the substitution of carbon‐intensive products with wood products can reduce greenhouse gas (GHG) emissions. Since wood is a limited resource, it must be used efficiently and sustainably. Shifting wood from one application to another might result in decreased GHG emissions due to substitution effects. However, which wood application will lead to a GHG emission reduction is currently unknown. This study investigates the effects of shifting wood between applications and the resulting substitution effects from a system perspective. A system dynamics model describes the wood utilization system of Austria, including the value chains from the forest to wood‐processing industries and the substitution that takes place in these industries. These value chains are associated with the global warming potential. Seven wood utilization scenarios shifting between material use and use for energy are simulated. The results show that wood shifts lead to both a substitution effect (emission reduction) in industries where wood utilization is increased and a counter effect (emission increase) where wood is replaced. The two effects potentially outweigh each other partly, leading to comparatively small net effects. However, carbon sequestration in HWPs and future changes in substitution effects might lead to additional effects. To substantially contribute to climate change mitigation, alternatives other than shifting wood between material and energy value chains need to be found within the wood utilization system. [ABSTRACT FROM AUTHOR]

With the signing of the Paris Agreement, which aims to limit global warming to 1.5°C by reducing greenhouse gas emissions and promoting climate adaptation, Colombia has demonstrated its commitment to the environment. This aligns with Sustainable Development Goals (SDGs) 7 and 13, which promote the use of sustainable energy and the reduction of CO2 emissions. Colombia's decarbonization strategy, defined in the 2021 hydrogen roadmap, outlines how to implement low‐emission hydrogen production to reduce GHG emissions by 2030. Considering Colombia's wind potential, an analysis of hydrogen production in the Department of La Guajira using this renewable energy was conducted. System dynamics was used to represent the hydrogen production infrastructure from wind energy with a polymer electrolyte membrane (PEM) electrolyzer, considering the current and projected capacity of the wind farm. The results obtained from this simulation show a reduction in CO2 emissions compared to the baseline scenario. Five scenarios were explored to analyze the simulation results. The data revealed that from 2022 to 2050, CO2 emissions (kg) were reduced by between 5550 and 10,170. Additionally, the potential for hydrogen generation in the northern region of the country is highlighted, along with recommendations for decision‐making related to production capacity. [ABSTRACT FROM AUTHOR]

In this study, evolutionary game method, prospect theory, and system dynamics (SD) method are combined to analyze the key stakeholders' behaviors in the public-private partnership project of not-in-my-back-yard facility. First, the interaction behavior of the public sector, the private sector, and the public and their equilibrium state was analyzed based on the evolutionary game method and prospect theory. Then, the SD method was used to simulate and analyze the impact of different variables on the behavior of the three stakeholders. The results show that the greater the perceived cost difference between the active and negative behavior among the three stakeholders, the more likely they are to take negative behavior. The private sector tends to act opportunistically under low-risk loss situations. Dynamic rewards are more likely to incentivize the public to supervise than high rewards. Some recommendations to promote active behavioral interactions and cooperation among stakeholders were presented accordingly. [ABSTRACT FROM AUTHOR]

The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying the interactions of neurons. Estimating parameters of this class of generative model allows one to predict the system's dynamics and responses under changing inputs and, indeed, changing parameters. We first assume a set of known state-space equations and address the problem of inferring the lumped parameters from observed time series. Crucially, we consider this problem in the setting of bistability, random fluctuations in system dynamics, and partial observations, in which some states are hidden. To identify the most efficient estimation or inversion scheme in this particular system identification, we benchmark against state-of-the-art optimization and Bayesian estimation algorithms, highlighting their strengths and weaknesses. Additionally, we explore how well the statistical relationships between parameters are maintained across different scales. We found that deep neural density estimators outperform other algorithms in the inversion scheme, despite potentially resulting in overestimated uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. We then eschew the mean-field approximation and employ deep neural ODEs on spiking neurons, illustrating prediction of system dynamics and vector fields from microscopic states. Overall, this study affords an opportunity to predict brain dynamics and responses to various perturbations or pharmacological interventions using deep neural networks. [ABSTRACT FROM AUTHOR]

The complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks. [ABSTRACT FROM AUTHOR]

This article introduces an advanced nonlinear controller designed for optimizing the performance of a single‐link robot arm featuring a flexible joint. The proposed nonlinear control strategy incorporates a Proportional‐Integral (PI) controller in conjunction with a nonlinear velocity feedback component, aimed at providing effective nonlinear damping and suppressing vibrations. To validate the controller's performance, extensive simulations are conducted utilizing machine learning techniques within the Python environment. The performance of the proposed nonlinear damping controller is benchmarked against a conventional linear cascaded P‐PI control structure, with both controllers fine‐tuned using the Nelder‐Mead algorithm. Simulation results demonstrate that the nonlinear damping controller yields substantial improvements in the dynamic behavior of the robot axis arm, showcasing superior step and sinusoidal position tracking performance, along with active vibration damping capabilities. This research contributes valuable insights into the enhanced nonlinear control strategies for flexible‐joint robot arms, offering promising advancements in their overall dynamic performance. [ABSTRACT FROM AUTHOR]

In this article, we present least fractional nonlinearity for exhibiting chaos in a memristor-based hyper-chaotic multi-stable hidden system. When implementing memristor-based systems, distinct dimensions/order define the memristor nonlinearity. In this work, the memristor dimension has been changed fractionally to identify the lowest order of nonlinearity required to induce chaos in a proposed system. The two-parameter frequency scanning helps in understanding both oscillation and non-oscillation regimes. The system fractional nonlinearity strength will help in deeper understanding of mathematical modelling and control. In addition, multistability and hidden oscillations were thoroughly investigated in the proposed system. The current work combines analytical, numerical, and experimental methods to demonstrate the system dynamics. [ABSTRACT FROM AUTHOR]

ABSTRACT In this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of a model called the (2 + 1) q -deformed tanh-Gordon model. This model is particularly useful for studying physical systems with violated symmetries, as it provides insights into their behavior. To solve the (2 + 1) q -deformed equation for specific parameter values, the (H + G ′ G 2) -expansion approach is employed. This technique generates analytical solutions that reveal valuable information about the system's dynamics and behavior. These solutions offer insights into the underlying mathematics and deepen the understanding of the system's properties. To validate the accuracy of the analytical solutions, the finite difference technique is also used to find a numerical solution to the q -deformed equation. This numerical approach ensures the correctness of the solutions and enhances the reliability of the results. Tables and graphics are presented in the publication to aid comprehension and comparison. These visuals improve the clarity and interpretability of the data, allowing readers to better understand the similarities and differences between the analytical and numerical solutions. [ABSTRACT FROM AUTHOR]

Multi-type birth–death processes underlie approaches for inferring evolutionary dynamics from phylogenetic trees across biological scales, ranging from deep-time species macroevolution to rapid viral evolution and somatic cellular proliferation. A limitation of current phylogenetic birth–death models is that they require restrictive linearity assumptions that yield tractable message-passing likelihoods, but that also preclude interactions between individuals. Many fundamental evolutionary processes – such as environmental carrying capacity or frequency-dependent selection – entail interactions, and may strongly influence the dynamics in some systems. Here, we introduce a multi-type birth–death process in mean-field interaction with an ensemble of replicas of the focal process. We prove that, under quite general conditions, the ensemble's stochastically evolving interaction field converges to a deterministic trajectory in the limit of an infinite ensemble. In this limit, the replicas effectively decouple, and self-consistent interactions appear as nonlinearities in the infinitesimal generator of the focal process. We investigate a special case that is rich enough to model both carrying capacity and frequency-dependent selection while yielding tractable message-passing likelihoods in the context of a phylogenetic birth–death model. [ABSTRACT FROM AUTHOR]

Substructuring is a term used to describe the estimation of the dynamics of a coupled system assembly when only the dynamics of each uncoupled component is available. Existing approaches allow for the coupling of physical-to-physical models, physical-to-modal models, modal-to-modal models referred to as Component Mode Synthesis (CMS), and impedance-to-impedance models referred to as Frequency Based Substructuring (FBS). Often times, the component information may not be just modal data for both components or just FRF data for both components so that modal substructuring or FRF substructuring can be performed. In these cases, the component data needs to be converted from either modal data or FRF data to match the data of the other component. A method for directly coupling impedance- and modal-based components has not yet been addressed. A proposed Impedance to Modal Substructuring (IMS) approach addresses this situation by writing the equations in a form that allows the user to directly utilize modal data for one component and FRF data for the other component, offering more flexibility in coupling different component data sets. While intended to be used with experimental data, this approach may also implement analytical components. In this work, an approach was developed to allow for the direct coupling of impedance and modal models without the need for the user to convert component data type. The IMS approach derived in this work was validated using analytical and experimental data with various models. [ABSTRACT FROM AUTHOR]

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

We report ultrafast reflectivity measurements of the dynamics of the order parameter of the charge density wave (CDW) in TbTe3 under anisotropic strain. We observe an increase in the frequency of the amplitude mode with increasing tensile strain along the a -axis (which drives the lattice into a > c, with a and c the lattice constants), and similar behavior for tensile strain along c (c > a). This suggests that both strains stabilize the corresponding CDW order and further support the near equivalence of the CDW phases oriented in a - and c -axis, in spite of the orthorhombic space group. The results were analyzed within the time-dependent Ginzburg–Landau framework, which agrees well with the reflectivity dynamics. Our study presents an ultrafast approach to assess the stability of phases and order parameter dynamics in strained systems. [ABSTRACT FROM AUTHOR]

One of the main difficulties for application of sliding mode control is the presence of chattering. It is a well known fact that the amplitude of chattering is proportional to the magnitude of discontinuous control. The problem can be alleviated by making the magnitude as small as possible while still ensuring the existence of the sliding mode. In this paper, a novel predefined-time single layer adaptive scheme based on equivalent concept with a relaxed assumption about unknown dynamics is proposed for dynamically tuning the gain associated with the control magnitude, and applying this scheme in sliding mode control algorithms enables decreasing the control action magnitude to the minimum possible value remaining the characteristic of finite-time convergence. Simulation results are shown to verify the effectiveness of the designed scheme. [ABSTRACT FROM AUTHOR]

This study investigated the current and potential future impact of climate change on water resources in the Minjiang River Basin, Fujian Province, China. Coupling a statistical downscaling model (SDSM) model and the Soil and Water Assessment Tool (SWAT) model, and considering the Coupled Model Intercomparison Project (CMIP) CMIP5 Representative Concentration Pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, we found a notable upward trend (2.9%, 7.5%, and 12.3% increase, respectively) in multiyear average runoff. A system dynamics feedback model was applied to optimize water resource allocation for Fuzhou, Sanming, and Nanping cities in the Minjiang River Basin, considering the runoff predicted by the SWAT model under climate change. The prediction results show that industrial water use steadily decreases, whereas water consumption in the tertiary sector increases concurrently. By 2050, water allocation schemes have reached the level of moderately developed countries, with these schemes taking into consideration the constraints imposed by red lines for water usage upper limit in China. These findings provide insights into the current and potential challenges posed by climate change, and can guide effective water resource planning for Minjiang River Basin cities. [ABSTRACT FROM AUTHOR]

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

Student retention is a persistent problem in many educational institutions, and we seek to address this issue through the implementation of tutoring programs. To achieve this objective, system dynamics (SD) modeling is proposed as a method. This analytical tool allows simulating and predicting the behavior of a complex system over time, considering the interactions between its components. The main objective of this research is to perform SD modeling to improve student retention through tutoring. It seeks to design more effective and personalized tutoring programs, adapted to the specific needs and challenges of the institution's students. The results obtained show that, in the period between 2022 and 2026, research degrees will be encouraged, reaching 50% participation. This increase is considered a positive indicator that encourages universities to become research protagonists. In conclusion, SD modeling makes it possible to forecast and strategically plan the expected results in terms of student retention. This method provides tools to more effectively address the problem of retention, ensuring the academic success of students and promoting the participation of universities in research. [ABSTRACT FROM AUTHOR]

Nowadays, using social media has expanded in societies and organizations. So the question arises: What are the effects of social media on organizations? It seems the varied effects of social media on organizations altogether form a complex, dynamic system. This study examines this issue with system dynamics to model the dynamic complexity. The concept absorptive capacity is used as a mediator to apply the social media effects on the system. Absorptive capacity means the organization's ability to recognize valuable external information, assimilate, and utilize it in the organization, explaining the difference in the performance of organizations. After modeling, using the data obtained from a company active in the Iranian food industry, the relationships between the model variables are defined. To generalize the model, its variables are normalized so that the results can be applicable for different organizations. To improve the current situation, four policies are proposed and exerted to the model. The results demonstrate that proactively managing social media, compared to increasing the investment share of social media, has a more considerable effect on the organization's financial performance and market share. [ABSTRACT FROM AUTHOR]

The design of a flying vehicle autopilot poses a significant challenge to the controller performance and the effectiveness of the flight control system. This challenge arises from uncertainties and extreme variations in parameters. To enhance the performance of the flight autopilot, the application of adaptive control techniques becomes crucial. Flight control systems extensively employ adaptive control methods due to their capacity to swiftly and automatically adapt to environmental changes and system dynamics, consequently achieving greater speed and accuracy. This paper aims to simulate scenarios where in the flight control system of an flying vehicle approach, with a specific focus on aerodynamic control in the roll, pitch, and yaw channels. The main purpose of the paper is to be able to provide an adaptive flight control system that can perform properly in the face of sharp and sudden changes in parameters. The paper compares the performance of the multiple model adaptive control method, particularly the switching and tuning technique, for the autopilot's roll, pitch, and yaw with other methods such as the reference model adaptive control used in previous research. The results demonstrate the superiority of the multiple adaptive controller utilizing switching and tuning techniques in handling parameter changes, exhibiting faster and more responsive behavior compared to traditional adaptive controllers. [ABSTRACT FROM AUTHOR]