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Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME%5ftutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models. [ABSTRACT FROM AUTHOR]

Electroporation is a complex, iterative, and nonlinear phenomenon often studied through numerical simulations. In recent years, simulations of tissue electroporation have been conducted with static models. However, the results of a static model simulation are restricted to a fixed protocol signature of the pulsed electric field. In this paper, we describe a novel dynamic model of tissue electroporation that also accounts for tissue dispersion and temperature to allow time-domain simulations. We have implemented the biological dispersion of potato tubers and thermal analysis in a commercial finite-element method software. A cell electroporation model was adapted to account for the increase in tissue conductivity. The model yielded 12 parameters divided into three dynamic states of electroporation. The thermal analysis describes the dependence of tissue conductivity on temperature. The model parameters were evaluated using experiments with vegetal tissue (Solanum tuberosum) under electrochemotherapy protocols. The proposed model can accurately predict the conductivity of tissue under electroporation from 100 to 1000 V/cm. A negligible thermal effect was observed at 1000 V/cm, with a temperature increase of 0.89 ° C. We believe that the proposed model is suitable to describe the electroporation at the tissue level and provides a hint of the effects on the cell membrane. [ABSTRACT FROM AUTHOR]

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes. [ABSTRACT FROM AUTHOR]

The emerging processual view of institutions has eroded the assumption of institutional stability in favor of a more dynamic view of institutions as ongoing processes, thereby foregrounding the question of institutional stabilization. Grounded in a process ontology, we conceptualize institutions as ever-becoming yet enduring social processes that are meaningful and carry prescriptions for actors' legitimate participation. Building on this conceptualization, we develop a theoretical model of the role of temporality in institutional stabilization that explores how three dimensions of institutions (meaning, prescriptions, and participation) are each stabilized by a facet of temporality (temporal patterns, expectancies, and mechanisms), as well as factors affecting each of these links. Our arguments contribute to writing on institutions in relation to temporality, agency, and process. [ABSTRACT FROM AUTHOR]

This study purposes to extract some fractional analytical solutions of the Peyrard-Bishop-Deoxyribo-Nucleic-Acid (β -PBDNA) dynamic model with the beta-derivative by the unified Riccati equation expansion method (UREEM). Furthermore, we examine the role of various parameters of the fractional model on the soliton dynamic. The research focuses on computational biophysics and materials science, examining the impact of various parameters on the fractional model. This paper contributes to understanding soliton solutions and the β -PBDNA dynamic model, demonstrating the applicability of the UREEM method to various fractional models. Some soliton solutions of the model are successfully generated by applying the UREEM. Implementing the UREEM, we take a fractional wave transformation to convert the model into a nonlinear ordinary differential equation. So, a linear equation system is generated. After the system is solved, the soliton solutions are gained for the appropriate solution sets. Finally, 3D, 2D and contour graphs of diverse solutions are depicted at suitable values of parameters. In addition, this paper presents 3D, 2D and contour graphs of various solutions with suitable parameter values. The results are beneficial for interpreting the model in future work and confirm that UREEM is effectively applicable to diverse fractional models, coupled with a comprehensive graphical analysis of how different parameters influence these solutions. [ABSTRACT FROM AUTHOR]

Nanoplastics (NPs) are usually formed by the decomposition of large plastics, which will cause water pollution after entering the water body. Carbon filter column is used to adsorb and remove polystyrene nanoparticles (PSNPs). The influence of experimental conditions on adsorption was investigated and fitted by kinetic model. The results show that increasing the height of carbon filter column and decreasing the initial concentration of PSNPs and water flow rate can prolong the breakthrough time of carbon filter column. When the initial concentration of PSNPs is 0.8 mg L−1, the influent flow rate is 4 mL min−1 and the height of carbon filter bed is 8.5 cm, the removal effect is the best, and the depletion point of carbon filter column is extended to 48 h. Adams-Bohart model is suitable for describing the initial stage of adsorption. Thomas and Yoon-Nelson models can well describe the whole dynamic adsorption process of PSNPs, and Yoon-Nelson model can accurately predict the time required for 50% PSNPs to penetrate the carbon column. The adsorption mechanism of NPs by carbon filter column is mainly through the attachment sites and pore retention provided by particles on the surface of activated carbon. This study can provide new technical and theoretical support for the removal of NPs. [ABSTRACT FROM AUTHOR]

We develop a dynamic incomplete-markets model of entrepreneurial firms and demonstrate the implications of preferences for liquidity to entrepreneur's interdependent consumption, portfolio allocation, hedging and financing decisions. The numerical results provide several important implications. Preferences for liquidity reduce hedging demand using risky assets. Besides, the existence of preferences for liquidity decreases the implied equity value and encourages the entrepreneur to issue more debt. Especially, the preferences for liquidity can overturn the risk-shifting incentives of a risk-averse entrepreneur. [ABSTRACT FROM AUTHOR]

In this article, we analyse the forecasting performance of several parametric extensions of the popular Dynamic Nelson–Siegel (DNS) model for the yield curve. Our focus is on the role of additional and time‐varying decay parameters, conditional heteroscedasticity, and macroeconomic variables. We also consider the role of several popular restrictions on the dynamics of the factors. Using a novel dataset of end‐of‐month continuously compounded Treasury yields on US zero‐coupon bonds and frequentist estimation based on the extended Kalman filter, we show that a second decay parameter does not contribute to better forecasts. In concordance with the preferred habitat theory, we also show that the best forecasting model depends on the maturity. For short maturities, the best performance is obtained in a heteroscedastic model with a time‐varying decay parameter. However, for long maturities, neither the time‐varying decay nor the heteroscedasticity plays any role, and the best forecasts are obtained in the basic DNS model with the shape of the yield curve depending on macroeconomic activity. Finally, we find that assuming non‐stationary factors is helpful in forecasting at long horizons. [ABSTRACT FROM AUTHOR]

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate's prototype of the adaptive mirror of the Giant Magellan Telescope. [ABSTRACT FROM AUTHOR]

Continuous mode of adsorption using fixed bed column has been widely applied for wastewater treatment. Several dynamic mathematical models were used to analyze the adsorptive properties and breakthrough curve of the column system. This paper aims to highlight the underlying principles and applicability of three commonly used dynamic models, namely Thomas, Bohart-Adams and Yoon Nelson in estimating the behavior of continuous adsorption and its properties. The model equations were analyzed to unlock the root of derivation, and the findings from literature were summarized, compared and discussed. As opposed to the results from literature, it was revealed that the models studied are actually identical with direct relationships among the rate constants; hence, they must provide the same fitting regression. Three dynamic models were applied in a case study on column adsorption of rhodamine B by palm kernel shell activated carbon (surface area = 1775 m2/g), from which the results were presented and discussed by the analysis of breakthrough and fitting of dynamic models. The breakthrough time decreased with increasing initial dye concentration and flow rate, and vice-versa for bed height. The applicability of these dynamic models was demonstrated by the close agreement of qo from the models (33.9 mg/g − 69.3 mg/g) with the experimental ones (36.8 mg/g − 70.9 mg/g) with low percentage error. [ABSTRACT FROM AUTHOR]

The abnormal reduction of kelp production occurred in Rongcheng area of the Yellow Sea in 2021, which is closely related to the red tide. However, the relationship between the red tide event and the hydrodynamic environment remains unclear. In response to these issues, this study established a coupled physical-biological model which coupling the ROMS dynamic model with the RED_TIDE biological model for the Yellow Sea and Bohai Sea region, and discussed driving factors of this red tide. Based on this coupled model, various factors influencing the development of red tide were investigated, with a focus on analyzing the key factors contributing to the occurrence of the red tide event in the northern sea area of the Shandong Peninsula in 2021. The aggregation of dinoflagellate cells triggers red tide events. The distribution characteristics and concentration changes of dinoflagellate cells were studied by designing simulation experiments under different conditions to reflect the contributions of various influencing factors to red tide. According to observation data and simulation experiments, this study explored the effects of factors such as light conditions, tides, Yellow River runoff and wind field on the distribution and concentration of dinoflagellate cells. The variation of wind field can promote the proliferation and aggregation of dinoflagellate cells, serving as key factors in triggering red tide. The occurrence of red tide is a complex ecological phenomenon influenced by multiple factors, necessitating the comprehensive consideration of these factors to more accurately predict and prevent red tide events. [ABSTRACT FROM AUTHOR]

This paper presents a numerical and experimental study aimed at the modeling and dynamic characterization of the reinforced concrete structure of the Palazzetto dello Sport in Rome, designed and by Pier Luigi Nervi with Annibale Vitellozzi, and built by Nervi & Bartoli contractors in 1956‐57. An experimental dynamic testing campaign has been performed to obtain the modal properties of the structure, identified using operational modal analysis (OMA). The axial symmetry of the Palazzetto's dome, expected to exist in an idealized perfect dome, has been observed to occur experimentally by noting that rotated modes possessed nearly identical frequencies, evidenced by closely spaced double peaks in the power spectral density. This observation recognizes the remarkable precision of Nervi's construction methodology. A numerical 3D model has been developed by relying on detailed information about the structure gathered from various sources, including archival documents, on‐site testing, and surveying. The model has been calibrated by means of modal updating based on the experimental measurements collected in this study. The reconstruction of the dome using laser‐scanning and aerophotogrammetry has revealed a slight asymmetry in its thickness distribution (and consequently its mass distribution) that, when incorporated in the numerical model, has been shown to contribute to the experimentally observed frequency split. It is expected that, by tracking these closely spaced frequencies on top of the typical variations of natural frequencies in a health monitoring approach, further insight might be gained into the detection of possible damages and/or degradation of the structure and its components. [ABSTRACT FROM AUTHOR]

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknownmassmatrix, the proposedmethod enables e [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications. [ABSTRACT FROM AUTHOR]

Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes. [ABSTRACT FROM AUTHOR]

This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body. [ABSTRACT FROM AUTHOR]

In order to grasp dynamic characteristics of the kind of high-speed structural redundant parallel manipulator in actual work, the dynamic response of a novel high-speed structural redundant parallel manipulator considering multiple spatial revolute joints with radial and axial clearances is studied. Firstly, the dynamic model of the novel high-speed parallel manipulator considering the multiple spatial revolute clearance joints is developed and verified. Then, the influences of different factors on dynamic characteristics of parallel manipulator are explored respectively. This paper contributes to explore the effect of revolute joints clearance on total property of the manipulator. [ABSTRACT FROM AUTHOR]

Diagnosis of compound faults plays a significant role in evaluating the health status of a gearbox. The difficulty of compound fault diagnosis is due to the entanglement of fault symptoms. In this work, diagnosis of single and compound uniform wear and tooth root crack via gear condition indicators have been studied analytically. A three-dimensional dynamic model that can include several design parameters and working conditions has been employed to model the dynamic of compound faults for the first time. The effect of fault severity, noise, working conditions, and design parameters on the diagnosis efficiency have been investigated. The qualitative and quantitative assessment of fault diagnosis has been accomplished via t-SNE and Generalized Discriminative Value (GDV) measures, respectively. The results show that the gear condition indicators are more suitable to assess the general health state of the gearbox. However, their ability to detect the type of fault and decouple compound faults are limited. Furthermore, design parameters of the gear transmission system, such as helix angle and gear width, can highly affect the fault diagnosis efficiency. [ABSTRACT FROM AUTHOR]

For specific missions of spacecraft including on-orbit assembly, docking, grasp, etc., the presence of uncertainties leads to difficulties in the analysis of impedance force control systems and evaluations of safety for space manipulators. Therefore, in this study, a novel interval uncertainty-oriented impedance force control method for space manipulators with time-dependent reliability was established. The proposed method can accurately and rapidly characterize uncertainties. Additionally, it can reflect the reliability of a control system based on an overall time history. Once the uncertainties of the dynamic models and state responses are known, their uncertainty bounds can be obtained using an established non-probabilistic propagation method with high accuracy. An interval process model and the first passage theory are employed in the non-probabilistic time-dependent model. A joint reliability index is established based on the single reliability indexes, which can more efficiently evaluate the control system reliability than the individual indexes. A numerical example related to space manipulator is used to study the developed impedance force control method and verify the effectiveness of the method. The results from the response curves reveal validity of the proposed method, while the reduction in time proves efficiency of the method. • Uncertain impedance force control method for space manipulator is proposed. • A non-probabilistic reliability-based impedance force control method is proposed. • Uncertainty analysis of kinematics and dynamics is proposed with bounded parameters. • Uncertain impedance control is established using interval uncertainty propagation. • A non-probabilistic time-dependent reliability is constituted to assess response. [ABSTRACT FROM AUTHOR]

Space robots are inseparable components of many space missions. However, capabilities of space robots with rigid manipulators are restricted in confronting with unknown and confined environments and situations require high safety. To overcome these defects, the potential of bio-inspired soft robots for space applications has recently been addressed in the literature. Dynamic modeling and control of soft manipulators are challenging due to their infinite degrees of freedom. In this paper, a general comprehensive three-dimensional dynamic modeling framework is developed for a floating soft manipulator-spacecraft system employing the Euler-Lagrange method. This framework derives the coupled equations of motion adapting the generalized Jacobian approach. Consequently, the opportunity of exploiting model-based control methods will be provided. In addition, the proposed model avoids numerical singularities once the bending angles are near zero. Furthermore, the generalized Jacobian matrix is defined for any arbitrary point of the soft manipulator essential for the contact dynamics. Eventually, to verify the proposed dynamic modeling procedure, the dynamic response of a floating soft manipulator-spacecraft system released from an initial configuration is investigated. Then, a sliding mode controller is designed for the floating soft manipulator to track the desired shape and compared to the proportional-derivative control scheme. The obtained results follow the physical principles and fulfill the control objective. • To overcome defects of usual rigid manipulators, soft space robots are concentrated. • A thorough 3D modeling frame is presented for soft manipulator-spacecraft systems. • Thorough dynamic modeling process is established using the Euler-Lagrange approach. • The generalized Jacobian matrix is derived for any optional point of the soft arm. • Application of the sliding mode control is proven for the soft space manipulator. [ABSTRACT FROM AUTHOR]

Dynamic in vitro absorption systems and mechanistic absorption modeling via PBPK have both shown promise in predicting human oral absorption, although these efforts have been largely separate; this work aimed to integrate knowledge from these approaches to investigate the oral absorption of a RET inhibitor, pralsetinib, with BCS Class II properties. Tiny-TIM (TIM B.V., Weteringbrug​, The Netherlands) is a dynamic in vitro model with close simulation of the successive physiological conditions of the human stomach and small intestine. Tiny-TIM runs with pralsetinib were performed at doses of 200 mg and 400 mg under fasting conditions. Mechanistic modeling of absorption was performed in Simcyp V21 (Certara, Manchester, UK). Pralsetinib fasted bioaccessibility in the Tiny-TIM system was 63% at 200 mg and 53% at 400 mg; a 16% reduction at 400 mg was observed under elevated gastric pH. Maximum pralsetinib solubility from the small intestinal compartment in Tiny-TIM directly informed the supersaturation/precipitation model parameters. The PBPK model predicted a similar fraction absorbed at 200 mg and 400 mg, consistent with the dose proportional increases in observed pralsetinib exposure. Integrating dynamic in vitro systems with mechanistic absorption modeling provides a promising approach for understanding and predicting human absorption with challenging low solubility compounds. [ABSTRACT FROM AUTHOR]

The vehicle dynamic load can greatly affect the service life of pavement. To study the vehicle dynamic load accurately and conveniently under different pavement characteristics and driving conditions, a multibody dynamics method was employed to establish a three-dimensional (3D) vehicle dynamics simulation model of a six-axle heavy vehicle (the most typical heavy vehicle on an expressway). Experimental tests of vehicle component parameters and axle spindle loads were performed to provide simulation parameters and to validate this vehicle dynamics model, respectively. The validation results showed that the accuracy and robustness of the dynamics model were satisfactory. Based on the validated model, the effects of International Roughness Index (IRI) and vehicle speed on the dynamic load coefficient (DLC) were explored. The DLC increased by about 80% when the speed or IRI changed from 30 km/h or 1.184 m/km to 70 km/h or 3.551 m/km , respectively. To better evaluate the effect of dynamic load, the relationship between the DLC of the vehicle and the vehicle speed and IRI is proposed according to simulation results. This relation equation can quickly and accurately study the DLC of this heavy vehicle under different IRI pavements and driving speeds. Because the vehicle dynamics model is convenient for studying the dynamic load under different conditions, this method is recommended in vehicle–pavement coupled analysis. [ABSTRACT FROM AUTHOR]

• A new modeling approach for compressor blades is proposed by the Timoshenko beam theory. • The dynamic model of cracked blades is proposed considering the stiffness nonlinearity induced by breathing cracks. • The modeling method is verified by experiments and the proposed dynamic model is validated using ANSYS software. Aero-engine blades may crack under harsh operating environments. This reduces the reliability of the aero-engine and causes catastrophic accidents. Although dynamic modeling and vibration characteristics of simplified blades with cracks have been analyzed extensively, there are few studies on the actual compressor blade with varying sections and twisted shapes. Based on Timoshenko beam theory, a new modeling approach for the compressor blade is proposed. Next, according to the strain energy release rate and Castingliano theory, the dynamic model of cracked compressor blades is established considering the stiffness nonlinearity induced by the breathing crack. The modeling approach is validated by the measured natural frequencies of an intact blade. Then, the dynamic model of the cracked blade is verified by comparing the modal characteristics and vibration characteristics using ANSYS software. Finally, some results show that the natural frequencies f n1 , f n2 , and f n4 increase as the pre-twisted angle and stagger angle increase. Due to the breathing effect, some integer frequencies appear in the spectrum including 0 f e and 2 f e , and the variations in the amplitude of frequencies 0 f r and 2 f r are monotonic under different aerodynamic amplitudes. The research can provide some help for the modeling of the actual blade and fault diagnosis of cracked blades. [ABSTRACT FROM AUTHOR]

Summary: The unstable operation of a direct‐drive permanent magnet synchronous generator (DPMSG) is exacerbated by periodic oscillation, having a significant impact on the safe and stable functioning of wind energy generation systems. This paper proposes an approximate solution method for analyzing the periodic oscillation of the Hopf bifurcation and a method for suppressing its bifurcation through H∞ robust control. Firstly, a three‐dimensional nonlinear dynamic model of the DPMSG is constructed. Secondly, the Hopf bifurcation of the system with changes in internal parameters is solved using the time domain and frequency domain methods, and the harmonic balancing method is used to approximate the periodic solution at the point of Hopf bifurcation. The order of the Hopf bifurcation neighborhood is then decreased using the central manifold theorem. Finally, this study suggests a H∞ output feedback control employing bifurcation parameters. The Nyquist criteria are used for evaluating small signal stability and convergence speed of the response system in DPMSG before and after dimensionality reduction. The simulation results suggest that the proposed strategy helps to tackle the periodic solution expression and instability difficulties caused by Hopf bifurcation. This paper provides theoretical suggestions for the future reliable operation of new energy generation systems. [ABSTRACT FROM AUTHOR]

Dynamic origin–destination (OD) flow is a fundamental input for dynamic network models and simulators. Numerous studies have conducted dynamic OD estimations based on fixed detectors, where a high device coverage rate and data quality are often required to accomplish the desired results. Several existing methods have used probe vehicle trajectories as an additional data source, and generalized least squares (GLS) is commonly recognized as an effective framework. However, the prior matrices used in these models either came from historical data or data obtained by uniform scaling that neglected the variation in penetration rates and suffer from sparsity issues. Moreover, the microscopic information contained in the high-resolution probe vehicle trajectories has not been fully utilized. The possibility of estimating OD flows using only vehicle trajectories without external information is rarely discussed in current literature. Therefore, this paper introduces a dynamic OD flow estimation model solely using probe vehicle trajectories. In the proposed model, two methods based on probe OD pair distribution are proposed to infer prior OD flows. Then the GLS framework is extended by including link travel times as another objective term, and the solution algorithm is adapted to deal with uncertain priors. To validate the proposed model, extensive experiments were conducted on a simulation network. The results show that the proposed model could reliably estimate dynamic OD flows and showed superiority to two existing models. In sensitivity analysis concerning the penetration rate and degree of saturation, the proposed model presented satisfactory performance and could adapt to various conditions. [ABSTRACT FROM AUTHOR]

The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In most practical engineering applications, the translating belt wraps around two fixed wheels. The boundary conditions of the dynamic model are typically specified as simply supported or fixed boundaries. In this paper, non-homogeneous boundaries are introduced by the support wheels. Utilizing the translating belt as the mechanical prototype, the vibration characteristics of translating Timoshenko beam models with non-homogeneous boundaries are investigated for the first time. The governing equations of Timoshenko beam are deduced by employing the generalized Hamilton's principle. The effects of parameters such as the radius of wheel and the length of belt on vibration characteristics including the equilibrium deformations, critical velocities, natural frequencies, and modes, are numerically calculated and analyzed. The numerical results indicate that the beam experiences deformation characterized by varying curvatures near the wheels. The radii of the wheels play a pivotal role in determining the change in trend of the relative difference between two beam models. Comparing the results unearths that the relative difference in equilibrium deformations between the two beam models is more pronounced with smaller-sized wheels. When the two wheels are of equal size, the critical velocities of both beam models reach their respective minima. In addition, the relative difference in natural frequencies between the two beam models exhibits nonlinear variation and can easily exceed 50%. Furthermore, as the axial velocities increase, the impact of non-homogeneous boundaries on modal shape of translating beam becomes more significant. Although dealing with non-homogeneous boundaries is challenging, beam models with non-homogeneous boundaries are more sensitive to parameters, and the differences between the two types of beams undergo some interesting variations under the influence of non-homogeneous boundaries. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties' influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness. [ABSTRACT FROM AUTHOR]

An internal thermally coupled air separation column (ITCASC) process is an effective energy‐saving technology in the air separation process. However, a large economic investment is a crucial factor for the widespread use of this technology in practical applications. In this article, an alternative configuration design, namely, top‐integrated (T‐ITCASC), bottom‐integrated (B‐ITCASC), and top‐bottom‐integrated ITCASC (T‐B‐ITCASC) with a focus on energy savings and economic feasibility are studied. A rigorous optimization based on a nonlinear interior‐point algorithm was developed by integrating the dynamic model into the optimization formulation. In the context of ITCASC process design and optimization, numerical simulations demonstrated that T‐ITCASC, B‐ITCASC, and T‐B‐ITCASC configurations improved energy‐saving potential and reduced capital investment, compared to the F‐ITCASC and conventional air separation column (CASC) configurations. Among these optimized configurations, the T‐B‐ITCASC configuration is preferred. [ABSTRACT FROM AUTHOR]

A non‐collocated output tracking problem for a flexible string with tip payload at one end was studied in this paper, which is expected to describe by a wave equation coupled ordinary differential equation (ODE) system. The regulated output and the reference trajectory signal with unknown frequency and amplitude are non‐collocated with the boundary control input. An adaptive approach has been developed to determine the unknown frequency of a disturbance by constituting an auxiliary trajectory and an internal model structure. In order to regulate exponential output tracking, the proposed error‐based adaptive dynamic compensation controller is employed. Closed‐loop stability and well‐posedness is established by exploiting the principle of semigroup theory. Finally, simulation experiments are performed to show the proposed scheme is very effective. [ABSTRACT FROM AUTHOR]

The method, using a servicing spacecraft (SS) mounted with very flexible operation devices (FOD), has shown promising application in detumbling rotating satellites due to its unique advantage of compliant contact processes. The SS is highly flexible, and successful implementation of detumbling rotating satellites is dependent on the advanced controller with high performance. However, the controller design suffers from two problems: (i) since the uncertain dynamics arising from the FOD lead to difficulty in acquiring an accurate dynamic model of the SS, the conventional model-based controllers are unable to achieve high performance; (ii) the large deformation of the FOD caused by contact processes generates impactive disturbance that severely deteriorates the transient and steady-state performances. To address these problems, this paper proposes a prescribed performance based adaptive model-free control for the first time. In the controller design process, a finite-time prescribed performance function is designed to incorporate into the model-free controller, which ensures the user-specified error constraints concerning the convergence rate, overshoot, and tracking accuracy. Meanwhile, an adaptive estimation algorithm is developed to significantly enhance the robustness against the disturbance. Moreover, an anti-windup compensator is constructed to effectively handle the adverse effect of thruster saturation. Simulation results validate the effectiveness of the proposed controller, and further show that the SS can successfully detumble the rotating satellite while achieving prescribed performance. [ABSTRACT FROM AUTHOR]

This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces. • A novel extended state finite-time observer is designed to estimate the internal disturbance forces. • We estimate the dead-zone error by using a stochastic configuration network, and then design a Backstepping compensation finite-time controller. [ABSTRACT FROM AUTHOR]

In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error. • Finite-time adaptive robust controller for Macro–micro composite platform. • Design and implementation of extended state observer for piezoelectric hysteresis. • Design of gain adjustment process to simplify calculation. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed synchronization control method and an accelerated backstepping tracking control scheme for the multi-motor driving system (MMDS). In the first step, we create a dynamic model of the MMDS with complex nonlinear dynamics, encompassing elements such as the dead zone, frictions, and disturbances. Next, in order to tackle the challenge of load tracking, we fuse a speed function, a cosine barrier function, a second-order tracking differentiator (TD), and a disturbance compensator into the backstepping approach. Lastly, to address potential issues related to diverse torque inputs, which could result in the overload occurrences, we put forward a novel distributed synchronization control scheme. This scheme aims to achieve torque synchronization for the MMDS while simultaneously ensuring superior load tracking performance. In the distributed synchronization control, a communication network is built to achieve the local coupling and improve the synchronization efficiency, and a corresponding mean deviation coupling synchronization control scheme is designed. Lyapunov theory is utilized to demonstrate the stability of the introduced control scheme. The simulation experimental results for the MMDS show the effectiveness of the proposed scheme. • A dynamic model of the MMDS encompassing elements like the dead zone and frictions is built. • An accelerated backstepping tracking method is proposed to address load tracking concerns. • A distributed synchronization control scheme is designed for the large-scale complex MMDS. • Our scheme guarantees both the load tracking and synchronization performance of the MMDS. [ABSTRACT FROM AUTHOR]

This article proposes a preview-based robust path-tracking control technique for maintaining lateral stability and tracking performance of autonomous vehicles, particularly in the presence of external disturbances and modeling uncertainties. First, a vehicle-road dynamic model with tire norm-bounded uncertainty is developed, which includes time-varying velocities and preview distances. The lateral and yaw dynamic characteristics are also analyzed in the frequency domain. Subsequently, an optimal preview model corresponding to sideslip-yaw rate states and longitudinal velocities is formulated employing a fuzzy logic model, and the sideslip angle is estimated using a sliding mode observer. Furthermore, a linear parameter-varying (LPV)/ H ∞ path-tracking controller that satisfies the pole placement and performance constraint is constructed to guarantee robustness and lateral stability across the whole parameters space with the coexistence of external disturbances and parametric uncertainties. Finally, the simulation results demonstrate that the proposed controller substantially enhances tracking performance while also maintaining excellent lateral stability. • A vehicle-road dynamic model with norm-bounded uncertainty for path-tracking controller design is built and analyzed. • An optimal-preview model is innovatively produced by utilizing velocity and the sideslip-yaw rate state of the vehicle. • An LPV/ H ∞ path-tracking control structure integrated optimal-preview model is proposed to improve tracking performance. [ABSTRACT FROM AUTHOR]

Problem definition: We consider two business models for a two-sided economy under uncertainty: dealership and marketplace with fulfillment services. Although both business models can bridge the gap between demand and supply, it is not clear which model is better for the firm or for the consumers. Methodology/results: We show that, whereas the two models differ substantially in pricing power, inventory risk, fee structure, and fulfillment time, both models share several important features with the revenues earned by the firm from the two models converging when the markets are thick. We also show that, for thick markets, there is a one-to-one mapping between their corresponding optimal policies. Managerial implications: Our results provide guidelines for firms entering two-sided markets: when the market is thick, the two business models are similar; when the market is thin, they should carefully inspect a number of market conditions before making the choice. Funding: The research of G. Li is partially supported by the National Natural Science Foundation of China [Grants 72150002, 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. The research of N. Chen is partially supported by the Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of G. Gallego is partially supported by Collaborative Research Funding Hong Kong [Grant C6032-21G]. The research of P. Gao is supported by the National Natural Science Foundation of China [Grants 72201234, 72192805], Collaborative Research Funding Hong Kong [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0253. [ABSTRACT FROM AUTHOR]

Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness problem, cross-domain sequential recommendation (CDSR) are proposed. CDSR explore rich data from a source domain to improve performance of the target domain. However, most of the existing CDSR methods are difficult to capture the temporal context of sequences and only learn user preference based on interactions of single domain, which leads to suboptimal performance. To address these shortcomings, we propose a channel-enhanced contrastive cross-domain sequential recommendation model (C3DSR). To be specific, (1) we design a feature extractor, which extends attention to the channel dimension, to extract the user's channel feature and capture the temporal contextual relationships between sequences. Then we calculate the weights of each channel by using three SE-Res2Blocks and multiply it with the channel feature to obtain user preference. (2) We concatenate the user's single-domain representation, the cross-domain representation, and the user features to make CDSR. Contrastive learning is leveraged to enhance mutual information between two domains. Experimental results show that the proposed model achieves the significant improvement of performance compared with other CDSR models on Amazon and HVIDEO datasets. [ABSTRACT FROM AUTHOR]

We develop a dynamic investment model with loan guarantees wherein insurers face information disadvantages and learn about borrower quality. Borrowers signal their qualities through investment timing, which is characterized by the investment threshold and elapsed time. We derive the conditions for separating or pooling equilibria. We show that the separating investment threshold is constant and determined mainly by the maximum threshold preventing mimicry. If project risk is higher (lower) than the market growth rate, the pooling investment threshold declines (increases) with elapsed time, and learning enhances (reduces) the willingness of high‐quality borrowers to wait. Learning alleviates adverse selection and reduces guarantee costs. These effects are more pronounced with a greater uncertainty of the insurer on borrower quality. We reveal dual effects of waiting. The worse the market prospect, the higher the value of waiting in pooling outcomes. Fee‐for‐guarantee swaps are superior to equity‐for‐guarantee swaps in environments with marked information asymmetry. [ABSTRACT FROM AUTHOR]

Accurate deflection prediction of in-service bridges can be used to assess the overall structural stiffness and detect abnormal states in advance. The bridge structures, especially long-span bridges, experience varying environmental and operational conditions, including temperature, humidity, wind excitation, and traffic loads, as well as long-term material deterioration and stiffness degradation mechanisms, and therefore, their deformation behavior shows complex variation phenomena, which pose challenges to many current deflection prediction methods. To address this subject, a Bayesian dynamic difference model (BDDM) to predict bridge deflection behavior online is proposed in this paper, explicitly considering the effect of the nonstationarity of time series data under varying environmental and operational conditions. A novel dynamic difference model is first proposed to include the nonstationary residual term and provide a linear approximation of a complex nonlinear process. Then, the formulas for recursively updating the dynamic difference model based on Bayesian inference are proposed. The proposed method is first validated through a numerical application using simulated nonstationary time series data with a nonlinear trend, indicating that it can adaptively capture nonstationary variations, update noise variance estimations, and improve prediction accuracy. To further demonstrate its performance, the BDDM is employed to predict the daily maximum deflection of a real-world cable-stayed bridge using measured data, and its performance is compared with several existing methods. The findings reveal that the proposed method outperforms other methods in terms of prediction accuracy, and can be potentially implemented for an online monitoring and early warning system. [ABSTRACT FROM AUTHOR]

To passively achieve an inertial device with unidirectional force transmission similar to Bang Bang control, this study introduces a novel energy dissipation device known as the resettable‐inertia damper (RID). The ingenious motion principles of the RID, encompassing a rack‐and‐pinion, bevel gear commutation system, speed transmission, and eddy current damping, are elucidated in detail. In particular, a unidirectional rotational flywheel within the device selectively engages when the primary structure reciprocates. The physical mass of the flywheel undergoes conversion into an amplified inertia through the rack‐and‐pinion mechanism, which enables the enhancement of damping effects coupling the flywheel rotation and eddy current configuration. A coupled multibody dynamic model, combining the clutching effect, the flywheel inertia, and the rotational damping, is formulated to analyze the system with RID (RIDS). Currently, an analysis of the hysteretic behaviors of RID is carried out. To facilitate the design and evaluation of the performance of RIDS, an equivalent linearization method is proposed for RIDS. The feasibility of this simplified method is validated under harmonic excitation. Additionally, the study examines the performance of equivalent linear systems (ELSs) and RIDS under natural ground motions and stochastic stationary excitation in peak and variance responses levels, respectively. Comparison of RID with traditional inerter shows that RID can achieve a more pronounced control with less force transferred to the structure and with the potential to recover vibration energy, highlighting its unique advantages. [ABSTRACT FROM AUTHOR]

The notion of stationary equilibrium is one of the most crucial solution concepts in stochastic games. However, a stochastic game can have multiple stationary equilibria, some of which may be unstable or counterintuitive. As a refinement of stationary equilibrium, we extend the concept of perfect equilibrium in strategic games to stochastic games and formulate the notion of perfect stationary equilibrium (PeSE). To further promote its applications, we develop a differentiable homotopy method to compute such an equilibrium. We incorporate vanishing logarithmic barrier terms into the payoff functions, thereby constituting a logarithmic-barrier stochastic game. As a result of this barrier game, we attain a continuously differentiable homotopy system. To reduce the number of variables in the homotopy system, we eliminate the Bellman equations through a replacement of variables and derive an equivalent system. We use the equivalent system to establish the existence of a smooth path, which starts from an arbitrary total mixed strategy profile and ends at a PeSE. Extensive numerical experiments, including relevant applications like dynamic oligopoly models and dynamic legislative voting, further affirm the effectiveness and efficiency of the method. [ABSTRACT FROM AUTHOR]

We document considerable cross-investor variation in survey expectations about aggregate stock market returns. Although most investors are extrapolators who expect higher returns after a good market performance, some are contrarians who expect lower returns after a good performance. More notably, compared with extrapolators, contrarians have less persistent expectations that are corrected more quickly. We then develop a dynamic equilibrium model accounting for these differences in expectations and find that the equilibrium stock price exhibits short-term momentum and long-term reversal as in the data. Furthermore, we test the key predictions of our model linking the observable differences in extrapolators' and contrarians' expectations to aggregate stock market momentum and future stock performance and find supportive evidence for our model mechanism. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4960. [ABSTRACT FROM AUTHOR]

This paper provides insights into the apparent seesaw between the generation of theoretical and empirical economics research over time. A dynamic model considers the incentives of researchers to focus on empirical versus theoretical papers. It yields the main characteristics of the path‐changing of economics research, from theoretical‐intensive to empirical‐focused. The model has two equilibria, one with a higher proportion of theoretical papers and another with a higher proportion of empirical papers. Curiously, the equilibrium with greater theoretical papers is stable, while the one with more empirical papers is a saddle point. This suggests that the current trend of increasing empirical research is unlikely to last. [ABSTRACT FROM AUTHOR]

The paper presents a case study in which a dynamic simulation modeling (DSM) calculation was carried out to assess the energy performance of a building in Giroc, a locality adjacent to the city of Timișoara, using the VABI Elements software. The characteristics were determined based on which the HVAC equipment was selected. [ABSTRACT FROM AUTHOR]

We assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. [ABSTRACT FROM AUTHOR]

This paper uses information at the daily, monthly, and quarterly frequencies to construct a daily World Economic Gauge (WEG). We postulate a mixed‐frequency dynamic factor model to extract data observable at different frequencies in order to track the health of the global economy. We show that the WEG offers a reliable basis for tracking economic activity during key events such as COVID‐19 and the Global Financial Crisis. Moreover, the WEG is shown to contain leading information about the output growth of the OECD, G7, NAFTA, European Union, and euro areas, in addition to the output growth of 42 individual countries. [ABSTRACT FROM AUTHOR]

Abstact: In this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors. [ABSTRACT FROM AUTHOR]

In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high‐frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models. [ABSTRACT FROM AUTHOR]

The article deals with the problem of a dynamic model of the truck crane considering the elasticity of the rope and the ground. The dynamic model of a truck crane in the vertical plane has been established. When considering the oscillation of a truck crane, it is a dynamic system with a large number of degrees of freedom. This model will allow us to evaluate the dynamic response of the machine, analyze the vibration of the heavy object being lifted and lowered, and subsequently study stability and control problems. In this paper, the dynamic models of the cranes in the working plane have been established, built by Lagrange's multiplier Equations, which are systems of differential-algebraic Equations with the generalized coordinates of the machine's motions and the elastic coordinates. The Lagrange Equation provides a simple method for solving dynamic problems. The advantage of this Equation is that the form and number of Equations do not depend on the number of objects in the investigated system, nor do they depend on the way the objects move. The number of Lagrange Equations depends only on the number of degrees of freedom of the system. [ABSTRACT FROM AUTHOR]

In response to the issues of vibrations affecting the construction safety, speed, and accuracy of suspended structures due to dynamic disturbances during lifting, environmental disturbances, and improper operator actions, this paper investigates the application of the Active Rotary Inertia Driver (ARID) system for double-rope suspended structures, namely analyzes, and applies control to the vibration response of double-rope suspended structures under external excitations. Taking the double-rope suspended structure as an example, a dynamic analytical model of the double-rope suspended structure is established based on D’Alembert’s principle. Through shake table tests, the dynamic response characteristics and vibration control effects of the double-rope suspended structure under various external excitations are studied, and the influence of the twisting radius of the suspended structure on the control effect is analyzed. The experimental results show that the ARID control system achieves significant control effects on the swing and torsional vibrations of the suspended structure, which validates the accuracy of the theoretical model for the ARID control system and demonstrates its feasibility for practical applications. [ABSTRACT FROM AUTHOR]