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The objective of this paper is to recommend an adjusted Susceptible-Exposed-Infectious-Removed (SEIR) model that characterizes the temporal patterns of various individuals affected by the omicron variant in an epidemic. This model considers factors such as vaccination, asymptomatic cases, indoor and outdoor air, hospitalizations, and deaths, as well as the impact of immunization and memory trace. While many recent studies overlook the complexities of multiple strains, including their diverse transmission rates and reaction to vaccines, this study introduces a new fractional derivative model to examine the spread of the omicron variant of COVID-19 and the implementation of a vaccination campaign. A thorough theoretical analysis is conducted, and the critical factor (R c r n z) is calculated using the model equations. It is demonstrated that when R c r n z is less than 1, the disease-free state is globally asymptotically stable, meaning that the epidemic diminishes. Moreover, the stability of both global and local equilibrium points is examined. Numerical simulations are employed to demonstrate the alignment between the numerical findings and theoretical characteristics. The model is adjusted to experimental data that reflect the progression of the omicron variant of COVID-19 in Guangzhou, exhibiting a satisfactory performance in predicting the number of infected individuals, thereby suggesting its capability to accurately estimate asymptomatic cases. Furthermore, to emphasize the benefits of employing fractional differential equations, the paper provides examples related to memory trace and hereditary characteristics. Moreover, the examined models are expected to be applied and expanded upon in order to contribute to the formulation of policies for disease control during times of limited vaccine availability. To summarize, the model appears to be a sufficient tool for researching and managing infectious diseases. It is projected that as the Omicron variant's prevalence declines, there will be a reduced need for respiratory-focused precautions. • An SVEIAOPHRD model is put forward to mathematically analyze the spread of the omicron variation of COVID-19. • Mathematical criteria for determining the stability of the equilibria are established. • An efficient nonstandard approach for resolving the continuous problem is suggested and examined. • The numerical simulations validate the analytical and numerical outcomes depicted in this study. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

This paper presents an inverse finite element (FE) approach aimed at estimating multi-layered human penile tissues. The inverse FE approach integrates experimental force–displacement and boundary deformation data of penile tissues with a developed FE model and uses new experimental data on human penile tissue. The experimental study encompasses whole organ plate-compression tests and individual layer tensile and compression tests, providing comprehensive insights into the tissue's mechanical behaviour. The biomechanical characterisation of penile tissue is of crucial significance for understanding its mechanical behaviour under various physiological and pathological conditions. The FE model is constructed using the realistic geometry of the penile segment and appropriate constitutive models for each tissue layer to leverage the accuracy and consistency of the model. Through systematic variation of tissue parameters in the inverse FE algorithm, simulations achieve the best match with both force–displacement and deformed boundary results obtained from the whole organ plate-compression tests. Test results from individual tissue layers are also utilised to assess the estimated parameters. The proposed inverse FE approach allows for the estimation of penile tissue parameters with high precision and reliability, shedding light on the mechanical properties of this complex biological organ. This work has applications not only in urology but also for researchers in various disciplines of biomechanics. As a result, our study contributes to advancing the understanding of human penile tissue mechanics whilst the methodology could also be applied to a range of other soft biological tissues. This research uses a multi-target inverse finite element (FE) approach for estimating the material parameters of human penile tissues. By integrating experimental data and a realistic FE model, this study achieves high-precision constitutive model parameter estimation, offering key insights into penile tissue mechanics under various loading conditions. The significance of this work lies in the use of this inverse FE approach for fresh-frozen human penile tissues, to identify the mechanical properties and constitutive models for both segregated tunica albuginea and corpus cavernosum as well as intact penile tissue segments. The study's scientific impact lies in its advancement of the understanding of human urological tissue mechanics, impacting researchers and clinicians alike. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new Weibull log-logistic mixture distribution accumulation generation operator is established. • A novel grey model with multiple interaction effects is established. • The robustness and stability of the proposed method is proved and validated. • A new parameter estimation method is designed. • The proposed method is superior to other seven benchmark models in four case studies. Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot's path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory. • Developing a dynamic model of WMR using modified traction force. • Proposing slip estimator based on an intelligent learning for both longitudinal and lateral slip. • Eliminating the time delay in slip estimation and compensating for its effect in the same time step by predicting the slip amount. • Improved robot performance in trajectory tracking on slippery surfaces. [ABSTRACT FROM AUTHOR]

This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state–space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input–output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples. • Present a state estimator to estimate the states of bilinear state space systems. • Apply the hierarchical principle to handle the associated terms among sub-algorithms. • Present a data filtering-based multi-innovation gradient-based iterative algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment. • A novel PIML with the state observer is proposed to learn physical equations. • A parameter estimation method is developed for the time-varying uncertain systems. • The performance of the parameter-dependent controller is verified by the experiment. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved enhancement space-variant anisotropic PDE-constrained for image denoising, based on a learning optimization procedure. Since the tensor structure of the Weickert-type operators encodes three critical parameters: λ 1 , λ 2 and θ , which define the local direction geometry in the image and also controls smoothing intensity along image features. We have then adopted an automatic estimation of these parameters based on a PDE-constrained optimization with a learning step of the additional information about the clean image. For the numerical solution of the computed free-noise image, we introduce a non-smooth Alternating Direction Method of Multipliers (ADMM) algorithm. The numerous tests in the experiments part incense, visually and quantitatively, the incomes of this new spatial tensor for PDE smoothing over the other denoising approaches. • An improved enhancement space-variant anisotropic PDE-constrained for image denoising is introduced. • An automatic estimation of the diffusion-tensor parameters based on a bilevel optimization with a learning step is involved. • A non-smooth ADMM algorithm is also performed to compute the solution of the optimization-constrained problem. • The comparative results demonstrate the performance of the proposed algorithm against complex noise structures. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

Strong relation between reliability studies and product quality. And reliability analysis deals with failure time of units and there are many well-known lifetime distributions. In this paper, we adopt the statistical inference of one more flexible one parameter distribution called Suja lifetime distribution. We primarily take into account four methods to estimate the distributional parameter other than the maximum likelihood estimation. The approximate confidence interval and two bootstrap confidence intervals have also been determined. The point and interval estimators of the Suja' parameter are assessed and compared throughout the Monte Carlo simulation study. Finally, the Suja distribution is used to modeled and analyzed three real data sets. [ABSTRACT FROM AUTHOR]

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE = 0.99 for the V-Tilted catchment, RMSE = 830 mg L-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE = 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment. [Display omitted] • An automatic calibration algorithm for distributed flood and water quality modeling is developed. • It uses HydroPol2D model and calibrate water quantity and quality parameters globally. • Data from observed gauges such as discharges, depths, and concentration is used for calibration. • Poorly placed gauges and low runoff events can increase equifinality during calibration. [ABSTRACT FROM AUTHOR]

Wind farm (WF) is gradually required to provide inertial support during frequency events nowadays. Existing WF inertial control methods have exhibited limitations in parameter estimation and optimal control parameter tuning under variable operating conditions. Moreover, traditional model-based methods suffer from computational efficiency problems due to their reliance on repeated iterations or derivative computations in optimization process. Thus, this paper proposes an inertial control method for large-scale WF based on hierarchical distributed hybrid deep-reinforcement learning (HDH-DRL). Firstly, the control objectives for the inertial control are defined, and are decomposed on the basis of wind turbines (WTs) division. Next, this paper presents a HDH-DRL based inertial control framework consisting of two levels. The upper-level control is achieved by the hybrid multi-agent DRL (HMA-DRL) algorithm with hybrid action exploration mechanism and multi-agent coordination. The consensus based lower-level control aims to achieve the consensus convergence of the control through the locally distributed interaction among WTs. Finally, the inertial control processes of the model are exhibited; the influences of DRL algorithms on the control are discussed; the computational speed and solution accuracy of the proposed method are compared with the model-based methods; the multi-scenario applicability and performance in local communication failures of the model are analyzed. The results demonstrate that the proposed method significantly improves the active power response and frequency stability, achieving rapid consensus convergence with a power deviation below 0.3%, surpassing traditional control methods by at least 0.2%; also, it demonstrates superior performance across various scenarios, including transient and steady-state conditions, with frequency enhancements up to 0.30% and exceptional stability under different wind conditions and communication disruptions. • Introduces HDH-DRL for improved WF inertial control and frequency stability. • Develops a bi-level HDH-DRL framework for faster, accurate control actions. • Utilizes HMA-DRL with a novel exploration mechanism for complex WF operations. • Achieves better accuracy and quicker computation than model-based methods. • Ensures control robustness across varied wind conditions and communication breaks. [ABSTRACT FROM AUTHOR]

In recent decades, computer vision-based methods have been introduced into engineering structures to provide full-field, non-contact vibration measurement. However, the vision-based vibration measurement method is challenging to measure the targetless structures. This paper proposed a target-free out-of-plane vibration measurement method using projection speckle and camera self-calibration technology. In this method, the camera extrinsic parameter estimation result based on the direct method is used as the initial value in the proposed method. Then, a nonlinear inequality constraint on the extrinsic parameters is established regarding the epipolar condition to minimize the reprojection error. Furthermore, the projection relationship between the projection speckle displacement and the actual displacement of the structure is studied so that the deviation of the out-of-plane vibration displacement is corrected. In the experimental verification, 3D reconstruction and rigid translation experiments are designed, respectively. The results show that the self-calibration accuracy of the binocular camera proposed in this paper is notably improved compared with the existing methods. At the same time, the deviation correction effect of the vibration displacement is significant. Finally, the typical flat plate structure and composite wing structure are taken as vibration measurement cases to demonstrate that the proposed approach is reliable and accurate in measuring the dynamic responses of engineering structures. • A target-free vision-based method using projection speckle and camera self-calibration is proposed for structural vibration measurement. • To increase the accuracy of camera self-calibration, a nonlinear inequality constraint is implemented to minimize reprojection errors. • The correlation between speckle and actual displacement is established, correcting the out-of-plane deviation. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SOC) estimation is crucial for ensuring the safe and reliable operation of battery management systems (BMS). Among the various algorithms used for SOC estimation in real-vehicle BMS, the extended Kalman filter (EKF) algorithm holds significance due to its adherence to optimal estimation principles and its property of insensitivity to initial values. By studying the relationship between error sources and SOC estimation errors, it becomes possible to develop targeted measures for enhancing the accuracy of SOC estimation based on the EKF. From a probabilistic perspective, this paper derives theoretical equations that establish the connection between SOC estimation errors and various error sources, including measured voltage, measured current, open circuit voltage curve, capacity, ohmic internal resistance, and polarization resistance. Furthermore, the paper analyzes the relationship among multiple error sources in generating SOC estimation errors. Building upon the outcomes of this theoretical analysis, a joint SOC estimation method that combines the EKF with Ampere-hour counting (AHC) is employed to identify errors. In scenarios where simultaneous faults occur in the current and voltage sensors, they are identified based on the slope and Euclidean distance of the two SOC trajectories, respectively. Subsequently, sensor faults correction and SOC compensation are implemented by leveraging simplified equations involving capacity and SOC increments and measured voltage and SOC estimation errors. In addition to addressing sensor faults, the paper also considers battery model parameter errors. By incorporating customized current pulse profile and theoretical equations relating to error sources and SOC estimation errors, a comprehensive error estimation of model parameters is achieved, capable of handling single and multiple errors. The derived simplification bridges the gap between error sources and SOC estimation errors, offering a novel approach for parameter sensitivity analysis and a theoretical foundation for quantifying these errors. The experimental results demonstrate the effectiveness and rapidity of the proposed method for identifying and correcting sensor faults and model parameter errors. • Derive error equations of signal error sources and SOC errors. • Analysis the relationship of multiple error sources in generating SOC errors. • Achieve the identification-correction of current and voltage sensor faults. • A model parameter errors estimation method is demonstrated. • A new method for parameter sensitivity analysis. [ABSTRACT FROM AUTHOR]

Achieving the objectives of sustainable development in water and agri-food systems requires the utilisation of decision-support tools in stakeholder-driven processes to construct and simulate various scenarios and evaluate the outcomes of associated policy interventions. While it is common practice to involve stakeholders in participatory modelling processes, their comprehensive documentation and the lessons learned remain scarce. In this paper, we share our experience of engaging stakeholders throughout the entire system dynamics modelling process. We draw on two projects implemented in the Volta River Basin, West Africa, to understand the dynamics of water and agri-food systems under changing environmental and socioeconomic conditions. We outline eight key insights and lessons as practical guides derived from each stage of the participatory modelling process, including the pre-workshop stage, problem definition, model conceptualization, simulation model formulation, model testing and verification, and policy design and evaluation. Our findings demonstrate that stakeholders can actively contribute to all phases of the system dynamics modelling process, including parameter estimation, sensitivity analysis, and numerical simulation experiments. However, we encountered notable challenges, including the time-intensive nature of the process, the struggle to reach a consensus on the modelled problem, and the difficulty of translating the conceptual model into a simulation model using stock and flow diagrams – all of which were addressed through a structured facilitation process. While the projects were anchored in the specific context of West Africa, the key lessons and insights highlighted have broader significance, particularly for researchers employing PSDM in regions characterised by multifaceted human-environmental systems and where stakeholder involvement is crucial for holistic understanding and effective policy interventions. This paper contributes practical guidance for future efforts with participatory modelling, particularly in regions worldwide grappling with sustainable development challenges in water and agri-food systems, and where stakeholder involvement is crucial for holistic understanding of the multiple challenges and for designing effective policy interventions. [Display omitted] [ABSTRACT FROM AUTHOR]

Intraspecific variability plays a key role in modeling population dynamics and ecological interactions. It is also a fundamental issue for agriculture, livestock production, and aquaculture. Dynamic Energy Budget (DEB) theory is a metabolic theory that can provide insights for these applications, as it can accurately predict the response of an organism to changes in environmental conditions. However, DEB model parameters are typically estimated for the species using mean trait values, neglecting individual differences. With advancing data collection technologies, there is a growing need to leverage individual data to calibrate DEB models. In this paper, we propose a multi-tier methodology for estimating individual-specific DEB parameters based on techniques and principles of the standard DEB estimation procedure. Individual data is often not sufficiently complete, therefore we compliment it available data on organisms of the same species. To avoid estimation bias, we employ a hierarchical tier structure. Data is categorized into tiers, depending on how general it is. In each tier, only a subset of the parameters is estimated depending on available data. The estimation proceeds in a top-down approach, starting with the most general tier and culminating in the individual tier. Values obtained in previously estimated tiers are used as pseudo-data to anchor the parameter estimation. In this way, we obtain biologically sensible individual parameter estimates in an efficient manner. As a case study, we apply our methodology to a dataset that contains weight and feed intake measurements for 52 individuals of the Mertolenga cattle breed from two performance trials. We conclude that variability in the surface-specific maximum assimilation rate and the digestion efficiency is needed to accurately model the individual data. Furthermore, the analysis of tier parameters and individual parameter distributions uncovers differences in the diets fed to the animals. • Methodology to estimate individual parameters for organisms in a DEB framework. • Hierarchical structure addresses information gaps and estimation bias. • Regularization ensures biologically reasonable parameter estimates. • Case study application on weight and feed intake data of Mertolenga cattle. • Parameter analysis uncovers differences in the diets fed to the animals. [ABSTRACT FROM AUTHOR]

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker's yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system. • A Fisher Information matrix driven (FIMD) method is proposed for optimization-free experimental design. • Iterative selection of the most informative experiments is leveraged by a ranking of experiments approach. • The method allows to achieve a fast reduction of uncertainty on model parameters. • FIMD method outperforms standard MBDoE techniques in terms of required computational time. • The method is tested on two case studies characterized by models of increasing complexity. [ABSTRACT FROM AUTHOR]

This paper develops a new method for lifelong learning referred to as Sparsify Dynamically Expandable Network (SDEN) via Variational Dropout, which explores a sparse model while preserving the performance. Dynamically Expandable Network (DEN) can learn a sequence of tasks via performing network retraining, network expansion by adding only the necessary neurons, and network split to effectively prevent semantic drift in an online manner. To overcome point estimation of parameters in DEN, Bayesian Compression for DEN is developed under the Bayesian framework. However, this method demands more time for model training and testing. To improve the model efficiency, we propose SDEN under the efficient sparse learning framework. We validate our SDEN in the lifelong learning scenarios with multiple frequently used benchmarks, on which it can obtain comparable classification accuracy, and less training and testing time compared with the comparison methods. Furthermore, our method can also learn a more sparse network structure which means fewer network parameters. • Comprehensively consider model performance, parameter storage space, and model training and test time. • Dynamically increase network capabilities with necessary number of neurons via Variational Dropout. • Achieve similar performance with comparative lifelong learning algorithms. • Obtain almost the minimum training and test time while considering the parameter quantity. • Learn the probability of removing a parameter or neuron from the dataset. [ABSTRACT FROM AUTHOR]

This paper mainly studies the parameter identification problem of multiple-input and single-output Hammerstein controlled autoregressive moving average (MISO-HCARMA) system in multivariable systems. A parameter identification method based on chaotic dynamic disturbance sand cat swarm optimization (CDD-SCSO) algorithm is proposed to solve the identification problem of a large number of unknown parameters. The novelty of this method is that it transforms the problem of model parameter identification into an optimization problem of nonlinear functions, and the problem of multiple-input nonlinear parameter coupling is solved by using swarm intelligence algorithm. By performing initial value chaotic map to the basic sand cat swarm optimization (SCSO) algorithm and adding a dynamic disturbance factor in the process of parameter optimization process, CDD-SCSO can better search for the optimal value of system parameters. The improved swarm intelligence algorithm can find the best estimate of model parameters through efficient optimal value search capability and parallel computing capability. Two numerical simulation examples and the case study of oxidation furnace incineration system demonstrate the effectiveness of the proposed algorithm for identifying MISO-HCARMA systems. [ABSTRACT FROM AUTHOR]

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique. We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers' simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 16% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers' yields a 140X speedup compared to the corresponding full order model. Python code to reproduce the results in this work is available at (https://github.com/MathBioCU/PyWSINDy%5fODE) and (https://github.com/MathBioCU/PyWLaSDI). • We introduce WLaSDI: Weak form Latent Space Dynamics Identification, a data-driven simulation framework that leverages WENDy: Weak form Parameter Estimation. • WLaSDI expands upon LaSDI: Latent Space Dynamics Identification in form and functionality. • We conduct a performance comparison between WLaSDI and LaSDI across several benchmark problems. • WLaSDI exhibits significantly greater robustness to noise compared to LaSDI. [ABSTRACT FROM AUTHOR]

The development of accurate and reliable mathematical models is the cornerstone for the modeling and optimization of processes. However, most of the existing models suffer from weak prediction capabilities due to poor data information content and poor parameter estimation methodologies. Several estimability approaches have been developed and increasingly implemented to address some of these issues. However, the wider adoption of these methods is still hampered by the lack of standardized and robust methodologies. In this paper, we present a Matlab Toolbox, called ESTAN, designed and developed to make estimability analysis accessible to non-specialist users. It uses a Quasi-Monte Carlo sequence to sample the main unknown parameters within their variation spaces. Then, depending on whether the studied model is computationally cheap or expensive, sensitivity indices are calculated either using the Sobol method or Fourier Amplitude Sensitivity Test (FAST). The calculated sensitivities are finally used within an orthogonalization algorithm to rank the parameters from the most to less estimable ones and to determine the estimable and non-estimable ones based on an estimability cut-off criterion. Various case studies are used to validate the toolbox and guide the users. The first one deals with the non-dynamic Toth adsorption model, while the second one deals with a dynamic batch cooling crystallization model. The main challenge with these two case studies is to show the importance of estimability analysis in the interpolation/extrapolation of model prediction capabilities. The last case addresses a computationally expensive thermodynamic model. The results for all the case studies are found to be promising, showing how the presented toolbox simplifies the investigation of the estimability analysis. [Display omitted] • A new global sensitivity-based estimability analysis Toolbox is proposed. • The toolbox ESTAN is validated based on three different mathematical models. • Model prediction capabilities are enhanced based on both interpolation and extrapolation. • ESTAN allows cost effective computations of the global sensitivities by switching from SOBOL to FAST. • ESTAN is an open-source package dedicated to non-commercial research. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach, the physics-informed long short-term memory (PI-LSTM) model, to address the forward and inverse problems of the frame structure equipped with the base isolation-fluid inerter system (FS-BIFI). Validation of the PI-LSTM model's effectiveness is demonstrated through a numerical case study of a single-story FS-BIFI. Employing this approach, the PI-LSTM model accurately predicted displacement and acceleration responses of a three-story FS-BIFI. Moreover, it conducts an evaluation of unknown damping-related parameters (β 1 , β 2) and a stiffness-related parameter (μ) of the fluid inerter mounted on the FS-BIFI. The model demonstrates a robust confidence value of 96.68% in predicting response error values within the range of [− 10%, 10%]. Notably, the relative errors in estimating unknown parameters (β 1 , β 2 , μ) stand at 12.760%, 8.857%, and 3.750%, respectively. The results show that the PI-LSTM model has great potential for response prediction and parameter inversion of complex structural systems. • The PI-LSTM model for response prediction and parameters estimation was established. • The applicability of PI-LSTM model for a single-story FS-BIFI was verified. • The displacement and acceleration responses of a three-story FS-BIFI were predicted. • The unknown parameters of the fluid inerter mounted on the FS-BIFIwere estimated. [ABSTRACT FROM AUTHOR]

The inverse heat transfer problems (IHTP) have a wide range of applications in the engineering field. Bayesian methods using Markov Chain Monte Carlo (MCMC) have long been considered as a robust and effective method for solving inverse problems. However, the discretization of the problem domain by the spatio-temporal Galerkin skill, i.e., the finite element interpolation also includes the time dimension, making the scale of the unknown parameters extremely difficult for Bayesian calculations. In this paper, a fast Bayesian parallel sampling (FBPS) framework is proposed for large-scale parameter estimation of benchmark three-dimensional inverse heat transfer problems (3D-IHTP). The FBPS we developed achieves a parameter computation scale of 10 5 magnitude within minutes, through dimensionality reduction of the space-time dependent problem domain. The Hamiltonian Monte Carlo (HMC) sampler, which is proven to be more efficient for high-dimensional parameter estimation, is employed. Through several simulation tests of IHTP, it was confirmed that the solving efficiency of FBPS surpasses that of the traditional Bayesian strategy significantly. Finally, FBPS is successfully developed to estimate the unknown heat flux on the chip heat sink and pack interface effectively, given some simulated high resolution measurement data. The reliability and efficiency show that FBPS has the potential to support efficient prediction techniques for a class of IHTPs in engineering applications. • A fast Bayesian parallel sampling framework (FBPS) for 3D-IHTP is proposed. • Computation time to solve IHTP with unknown size of 105 is in minute level. • FBPS is about 55 times faster than the traditional Gibbs method. • FBPS provides reliable technical idea to solving large-scale Bayesian inverse problem. • FBPS is applied to the inverse problem of chip packaging heat dissipation. [ABSTRACT FROM AUTHOR]

Accurate estimation of grid voltage parameters such as amplitude, phase angle and frequency is crucial for advance control and protection in power systems. There are different estimation techniques in the specialized bibliography, so the selection for their implementation involves analyzing a trade-off of operating characteristics such as accuracy, response time or complexity. This paper proposes a three-phase estimation algorithm with high fidelity performance during abnormal grid conditions. The proposed algorithm combines a new Two-Sample Sequence Extractor (TSSE) and a Recursive Least Mean Square (RLMS) frequency estimator in a mutual feedback mode to improve its operation. To estimate the amplitude and phase of the positive and negative sequence components, the TSSE performs a linear approximation between two voltage samples. Furthermore, a slide window approach RLMS is used to estimate the system's frequency. Simulation and experimental results are presented to analyze and validate the performance of the TSSE-RLMS. In addition, the proposed algorithm is compared with other four kinds of estimation methods under different grid conditions. • A 3 Φ voltage parameter estimator with high fidelity performance is presented. • The algorithm combines a Two-Sample Sequence Extractor and a RLMS frequency estimator. • The proposed algorithm is compared with other four kinds of estimation methods. • The TSSE- RLMS presents a fast and accurate estimation of the sequence voltages. [ABSTRACT FROM AUTHOR]

In the traditional portfolio models, the return rates of risky assets are usually defined as random variables or fuzzy variables. However, when some events such as financial crises or wars occur that can cause instability in financial markets, the results of these assumptions are not always satisfactory. Therefore, some scholars try to use uncertain variables to express the return rates. In this paper, a two-factor uncertain portfolio problem under optimistic value criteria is studied, where we use the optimistic value instead of expected value to describe the investment return and study the impact of economic circumstance factor and corporation's specificity factor on the return rates. Firstly, a two-factor optimistic value-variance-entropy model with background risk is constructed, in which the total investment return consists of the risky asset return and the background asset return. Then, considering the investors with two kinds of risk preferences, a three-step method is applied to convert the bi-objective optimization model. Moreover, a moment estimation method is used to process the expert-estimated data on the return rates. Finally, a numerical simulation is presented for showing the applicability of our models and solution method. • We use optimistic value to represent the return rather than expected value. • The multiple factors and background risk are considered simultaneously. • Three-step method is used to convert the model into two single-objective models. [ABSTRACT FROM AUTHOR]

This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system. Specifically, we adopt a mathematical "transport" model for the solution of the dynamical system at specific spatial locations that allows us to accurately estimate the model parameters, including those associated with structural damage. This is accomplished by means of a newly-developed mathematical transform, the signed cumulative distribution transform (SCDT), which is shown to convert the general nonlinear parameter estimation problem into a simple linear regression. This approach has the additional practical advantage of requiring no a priori knowledge of the source of the excitation (or, alternatively, the initial conditions). By using training data, we devise a coarse regression procedure to recover different PDE parameters from the PDE solution measured at a single location. Numerical experiments show that the proposed regression procedure is capable of detecting and estimating PDE parameters with superior accuracy compared to a number of recently developed machine learning methods. Furthermore, a damage identification experiment conducted on a publicly available dataset provides strong evidence of the proposed method's effectiveness in structural health monitoring (SHM) applications. The Python implementation of the proposed system identification technique is integrated as a part of the software package PyTransKit (Imaging and data science lab) [1]. • We investigate a mathematical model-based approach to identify PDE parameters of a dynamical system. • Existing solutions to this problem require a lot of sensor measurements. • We solve a different problem that utilizes minimum number of sensor requirement. [ABSTRACT FROM AUTHOR]

To improve the flowrate control accuracy of fuel control system at high supply pressure and large flowrate working condition, this paper proposes a novel dual closed-loop control for fuel metering unit (FMU) to address issues related to unknown velocity, matched/unmatched disturbances, and parameter uncertainties. Firstly, for a comprehensive assessment in flowrate feedback method, a global sensitive analysis is utilized to research the effect ratio of factors affecting the discharge flowrate. Then, a novel dual closed-loop control is proposed, in which the outer loop is flowrate loop, and a novel extended-state-observer (ESO)-based robust adaptive control without velocity sensor is used as the internal displacement loop control. Specifically, ESO can effectively estimate the unknown velocity and unmatched disturbance, and the parameter estimations can be updated by the adaptive law determined only by spool displacement and reference signal. The novel robust control can address matched/unmatched disturbances and model uncertainties effectively. Through Lyapunov method, it follows that the novel ESO-based robust adaptive control can achieve asymptotic tracking performance when occurring time-invariant disturbances and bounded tracking performance when occurring time-varying disturbances. Comparative experiments verify the superiority of the proposed control, which provides a new method and glimpse for the advanced control strategy of aeroengine. [Display omitted] [ABSTRACT FROM AUTHOR]

Aging modes analysis of lithium-ion batteries plays a crucial role in battery health management. The present studies for battery aging modes analysis are mainly based on mechanistic models or electrochemical models. However, most of the parameters of these models need to be measured offline, which adds difficulties to actual vehicle applications. Therefore, this paper proposes a method for analyzing and predicting battery aging modes based on a transfer learning method. Aging modes data of experimental batteries and electric vehicle batteries (EVBs) are obtained by an enhanced dual-tank model. Then, a transfer learning model based on long and short-term memory neural network is trained to achieve the analysis of EVBs aging modes by using the experimental battery aging data as the source data and the aging data of EVBs as the target data. The results show that the estimation error for the EVBs aging parameters is less than 6% and the predicted error for the next 5 months is less than 4%. The method offers a new idea for EVBs online aging modes analysis and prediction. • A EVBs voltage reconstruction method based on the internal resistance compensation method is proposed. • An enhanced dual-tank model can be used to estimate battery capacity while analyzing battery aging modes. • Transfer learning based model is used for aging parameters estimation of EVBs. • An unsupervised learning based transfer model is used for aging parameter prediction in EVBs. [ABSTRACT FROM AUTHOR]

In this paper, main objective is to develop a reduced-order adaptive state observer (RASO) for a large class of DC–DC converters with constant power load (CPL) to estimate their unavailable states and unknown parameter, which facilitates achieving an output feedback control (OFC) scheme of the systems. Its construction contains two main procedures. First, by using a generalized parameter estimation based observer (GPEBO) technique, state reconstruction problem is reformulated as the estimate of parameter after the system reparameterization. A linear regressor equation is then obtained. Second, we apply GPEBO technique for it again and then dynamic regressor extension and mixing (DREM) method are adopted such that a RASO for the converters described by a class of bilinear system is designed, which is called as 2G+D observer. The comparison study among the proposed observer and other existing observers is conducted. An obvious merit of the former is that the observability of the systems is only verified for ensuring exponential convergence without needing any extra excitation condition. Subsequently, the presented observer is applied to boost converter feeding unknown CPL for achieving an OFC. Finally, the simulation and experimental results are provided. • A reduced-order adaptive state observer is developed for a large class of DC–DC converters with CPL. • A unique condition for ensuring the convergence is that the considered system is only observable. • A forgetting factor is added to track slowly time-varying parameter variation. • The proposed observer is applied to boost converter with unknown CPL for achieving an output feedback control. [ABSTRACT FROM AUTHOR]

• The effectiveness of various retrofit techniques is discussed for timber bridges. • Suitable retrofit methods are identified in terms of capacity, energy, and failure characteristics. • Theory infers the possible range of feasible data to ensure the adequacy of research findings. This paper presents the effectiveness of various retrofit techniques in improving the flexural behavior of structural timber. For realistic representation with regard to site application, timber girders are salvaged from a decommissioned highway bridge that has been in service over several decades and are strengthened using lag bolts, carbon fiber reinforced polymer (CFRP) sheets, and hollow steel sections (HSS): the retrofitted girders are referred to as Bolt, CFRP, and HSS, respectively. Each category is repeated three times, and the responses of the 12 unstrengthened and strengthened girders are comparatively evaluated. From a load-carrying capacity point of view, lag bolting is not effective. However, CFRP and HSS result in a capacity increase of up to 156.2% relative to the control girder (Cont). Regarding the load–displacement relationship of these girders, the retrofit systems alter the post-peak behavior of the timber. For instance, the lag bolts that are periodically embedded along the specimens bring about pseudo-yield plateaus. The abrupt failure of the Bolt girder is thus precluded. In addition, the amount of dissipated energy increases in all upgraded girders owing to the presence of the retrofit systems. The failure modes of the individual girders are unique, depending upon the type of retrofit. Except for the HSS girder, the effective elastic moduli of the Cont, CFRP, and HSS girders agree with those measured by a stress wave timer. Parameter estimation theoretically infers the possible range of the experimental findings and ensures the adequacy of the test program. [ABSTRACT FROM AUTHOR]

In flood frequency analysis (FFA), choices of distribution and methods can hinder the reproducibility of results. Besides, changes in climate, land use/cover, and water management can induce nonstationarity. Frameworks to select between stationary FFA (S-FFA) and nonstationary FFA (NS-FFA) are lacking, and NS-FFA tools are limited. Therefore, this paper introduces a systematic and software-supported framework enabling repeatable workflows for both S-FFA and NS-FFA. The framework has three modules to a) process flood series for exploratory data analysis (EDA) and NS-FFA model determination (if needed), b) select the S-FFA or NS-FFA approach underpinned by the EDA, and c) perform FFA including model determination, parameter estimation, uncertainty quantification, and model performance assessment. The framework incorporates various distributions, methods, and metrics, and recent advancements in NS-FFA for model determination and uncertainty quantification and allows for the modeller's intervention while ensuring reproducibility. The software is freely available to the public. • A FFA framework enabling both the S-FFA and NS-FFA is proposed. • The EDA is employed to assist in the selection of the S-FFA and NS-FFA. • The framework contains decision points and allows for repeatable workflows. • The framework includes various models, metrics, and state-of-the-art methods. • The source codes and a user-friendly standalone GUI are freely available. [ABSTRACT FROM AUTHOR]

Unmanned tractor relies on multi-sensor information collection to obtain the current state or parameters. However, when driving in complex field, it will inevitably suffer from the uneven ground, the impact of crop straw and clods et al., which usually poses considerable challenges for multi-sensor to obtain stable and accurate value of states and parameters to realize tractor automatic lane guidance control. Therefore, a novel state and parameter estimation method by mixing the dual extended Kalman filter (DEKF) technology and adaptive radial basis function neural network (ARBFNN) technology is proposed in this paper. Firstly, DEKF technique is applied to estimate key states and initial model time-varying parameters–front/rear axle cornering stiffnesses at the same time. Then, in order to further improve the accuracy of estimation value of front/rear axle cornering stiffnesses during the automatic lane guidance control process, an ARBFNN technology is investigated by taking the heading error, lateral error and initial estimation value of front/rear axle cornering stiffnesses from DEKF as inputs to approach ideal estimation value. Finally, results of automatic lane guidance control scenarios from both simulation and hardware-in-loop (HIL) implementation show that the proposed hybrid estimated method of DEKF and ARBFNN can robustly obtain satisfactory estimation value and automatic lane guidance control performance for tractor when the control system is characterized by both time-varying model parameters and uncertain external energy-bounded disturbance. A comparative study is also conducted to investigate cases to show its effectiveness when the hybrid DEKF-ARBFNN state and parameter estimation method is used and when it is not. [ABSTRACT FROM AUTHOR]

Attitude motion parameters estimation of the space non-cooperative tumbling target is the premise of the on-orbit target capture task. However, existing methods cannot balance the estimation accuracy and efficiency. In this paper, an adaptive hybrid Kalman filter is presented to estimate the attitude, angular velocity and inertia motion parameters of the space non-cooperative tumbling target. First, the dynamic model of the non-cooperative tumbling target is derived, and the nonlinear discrete state-space equation is obtained. Then, a hybrid Kalman filter is designed by combining the extended Kalman filter and the unscented Kalman filter. The adaptive technique is introduced to track time-varying uncertain measurement noise caused by complex space disturbances. After that, based on the first-order linearization technique, the sufficient condition for the local convergence of the proposed method is proved. Finally, the numerical simulation experiment was conducted to compare the proposed method with the adaptive extended Kalman filter and the adaptive unscented Kalman filter. The experimental results show that the estimation accuracy of the proposed method is much higher than that of the adaptive extended Kalman filter and slightly lower than that of the adaptive unscented Kalman filter, and the iteration-average Central-Processing-Unit time is 64.2% smaller than that of the adaptive unscented Kalman filter and slightly longer than that of the adaptive extended Kalman filter. The proposed method achieves a much higher accuracy with a slight loss of iteration-average Central-Processing-Unit time. [ABSTRACT FROM AUTHOR]

Modern industrial processes are characteristic of nonstationary and uncertainty. To address these issues, this paper proposes a probabilistic principal component analysis based model that utilizes streaming variational inference for online monitoring of nonstationary process. The model parameters are updated in a streaming/batch-wise manner to track the nonstationary behavior in the process. In the updating procedures, the posterior distributions obtained from the previous data batch are used as priors for the current model, whose parameters can be estimated using the variational Bayesian inference. The streaming variational inference enables recursive parameter estimation over time. Additionally, in order to improve computational efficiency, a distributed update strategy is also applied to the proposed model. For online process monitoring, the noncentral chi-square distributions are derived to estimate the statistical control limits for each data batch, resulting in better adaptation to the nonstationary characteristics. The effectiveness and superiority of the proposed method are validated through simulation example and real-world industrial application. • Streaming variational PPCA model is proposed for monitoring nonstationary processes. • The model parameters are updated in a streaming/batch-wise manner. • The control limits are updated from the noncentral chi-square distributions. • The effectiveness is verified through simulation and application studies. [ABSTRACT FROM AUTHOR]

In this paper, a new approach of the Hot Disc method is presented based on the explicit calculation of the average temperature of a constant heat source on a semi-infinite medium. The Transient Plane Source (TPS) technique developed in the 90 uses a linear regression between the evolution of temperature of the source and a function. This function needs to perform an integral whose calculation can be complex and slow. Therefore, we propose a new approach based on the explicit analytical expression of the average temperature of the heat source to obtain simultaneously by inverse method, the thermal characteristics of the medium, i.e. the conductivity and diffusivity. Besides, we propose to estimate simultaneously the experimental unknown parameters, namely the correction time due to the sensor and the establishment of micro constriction phenomena and the thermal contact resistance between the sensor and the material to be characterized. This approach also makes explicit, for the first time, the sensitivities of the problem and thus to make a complete and accurate sensitivity analysis based on their analytical expressions. Thus, an uncertainty estimation analysis of the parameter estimation using a stochastic approach is carried out to predict confidence intervals on each parameter as a function of the measurement noise. A Monte Carlo method confirms the obtained results. An optimization of the experimental parameters is then conducted based on the minimization of the confidence interval of the estimated parameters. • A new approach of the TPS method is presented. • An explicit analytical calculation is used to perform the identification of the thermal conductivity and diffusivity. • A fully analytical sensitivity analysis is conducted. • A stochastic approach is carried out to predict confidence intervals on each parameter as a function of the measurement noise. [ABSTRACT FROM AUTHOR]

A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. One obvious strategy is to improve model structures utilising advancements in process knowledge and observations. Another statistically relevant approach is to develop improved error models so that the uncertainty can be more reliably estimated. The recently introduced river bed/bank storage (RBS) model is an improved representation of transmission losses/gains within basin-scale river system models. The novelty of the current research is the way the RBS model is combined with State and Parameter Uncertainty Estimation (SPUE) to produce more statistically reliable predictions. Using the RBS model with SPUE resulted in better matched predictive distributions in 13 out of 16 test cases, and higher proportions of observed values within the predictive ranges for all cases. The paper demonstrates that improving model structures combined with better characterisation of state error can alleviate issues of overfitting in predictions. • The study shows that extra parameters can lead to underestimation of uncertainty. • A state adjustment model inflates predictive uncertainty which counters overfitting. • Combining the RBS model with SPUE improved statistical reliability. • SPUE produced predictive distributions that were more consistent with observations. [ABSTRACT FROM AUTHOR]