Variation Of Model Parameters Research Articles

Bridging the Covid-19 data and the epidemiological model using the time-varying parameter SIRD model

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases’ effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic’s current stance. This superior performance, in turn, transforms into accurate predictions of the death cases and cases treated in Intensive Care Units (ICUs).

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure (PODs) for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Dynamical phase transition in models that couple chromatin folding with histone modifications.

Genomic regions can acquire heritable epigenetic states through unique histone modifications, which lead to stable gene expression patterns without altering the underlying DNA sequence. However, the relationship between chromatin conformational dynamics and epigenetic stability is poorly understood. In this paper, we propose kinetic models to investigate the dynamic fluctuations of histone modifications and the spatial interactions between nucleosomes. Our model explicitly incorporates the influence of chemical modifications on the structural stability of chromatin and the contribution of chromatin contacts to the cooperative nature of chemical reactions. Through stochastic simulations and analytical theory, we have discovered distinct steady-state outcomes in different kinetic regimes, resembling a dynamical phase transition. Importantly, we have validated that the emergence of this transition, which occurs on biologically relevant timescales, is robust against variations in model design and parameters. Our findings suggest that the viscoelastic properties of chromatin and the timescale at which it transitions from a gel-like to a liquidlike state significantly impact dynamic processes that occur along the one-dimensional DNA sequence.

Dynamic analysis and optimal control of stochastic information cross-dissemination and variation model with random parametric perturbations.

Information dissemination has a significant impact on social development. This paper considers that there are many stochastic factors in the social system, which will result in the phenomena of information cross-dissemination and variation. The dual-system stochastic susceptible-infectious-mutant-recovered model of information cross-dissemination and variation is derived from this problem. Afterward, the existence of the global positive solution is demonstrated, sufficient conditions for the disappearance of information and its stationary distribution are calculated, and the optimal control strategy for the stochastic model is proposed. The numerical simulation supports the results of the theoretical analysis and is compared to the parameter variation of the deterministic model. The results demonstrate that cross-dissemination of information can result in information variation and diffusion. Meanwhile, white noise has a positive effect on information dissemination, which can be improved by adjusting the perturbation parameters.

Chemical Potential Analysis as an Alternative to the van't Hoff Method: Hypothetical Limits of Solar Thermochemical Hydrogen.

The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications. However, by analyzing the oxygen partial pressure pO2, e.g., as measured by thermogravimetric analysis (TGA), this method convolutes the properties of the probe gas with the solid-state properties of the examined oxides, which define their suitability for specific applications. The "chemical potential method" is here proposed as an alternative. Using the oxygen chemical potential ΔμO instead of pO2 for the analysis, this method does not only decouple gas-phase and solid-state contributions but also affords a simple and transparent approach to extracting the temperature dependence of the reduction enthalpy and entropy, which carries important information about the defect mechanism. For demonstration of the approach, this work considers three model systems; (1) a generic oxide with noninteracting, charge-neutral oxygen vacancy defects, (2) Sr0.86Ce0.14MnO3(1-δ) alloys with interacting vacancies, and (3) a model for charged vacancy formation in CeO2, which reproduces the extensive experimental TGA data available in the literature. The reduction behavior of these model systems obtained from the chemical potential method is correlated with simulated results for the thermochemical water splitting cycle, highlighting the exceptional behavior of CeO2, which originates from defect ionization. The theoretical performance limits for solar thermochemical hydrogen within the charged defect mechanism are assessed by considering hypothetical materials described by a variation of the CeO2 model parameters within a plausible range.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I-V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I-V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I-V dataset and an I-V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

Investigation on the damage model of rock subjected to freeze-thaw cycles considering its fracture voids compaction

The investigation on the mechanical properties and damage model of rock subjected to freeze-thaw cycles (FTCs) holds significant theoretical for assessing the deformation of rock mass engineering in cold regions. When the rocks subjected to FTCs, the fracture voids of rocks increase with increasing number of FTCs, and the rocks exhibit nonlinear deformation characteristics as its fracture voids compaction. Based on the dissipation energy evolution law and distribution characteristics of rocks under loading, the critical point of rock compaction deformation was determined. A constitutive model for rock subjected to FTCs considering its fracture voids compaction was established based on macroscopic and microscopic damage levels. The rationality of the model was validated using experimental results, and a comparison was made between the calculation results of proposed model and model that not consider fracture voids compaction. The evolution properties of rock damage variable and stress under different FTCs were analyzed. The variation of model parameters with the FTCs and the influence of model parameters on the deformation characteristics of rock was analyzed.

Synchronization of two non-identical Chialvo neurons

We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neural network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two non-identical neurons are electrically coupled. In order to understand whether specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in each case. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in each case. Finally, we propose that the results have general applicability across various neuron models.

Pharmacogenetic guided versus standard warfarin dosing for routine clinical care with its pharmacoeconomic impact: a randomized controlled clinical trial.

Empirical use of pharmacogenetic test(PGT) is advocated for many drugs, and resource-rich setting hospitals are using the same commonly. The clinical translation of pharmacogenetic tests in terms of cost and clinical utility is yet to be examined in hospitals of low middle income countries (LMICs). The present study assessed the clinical utility of PGT by comparing the pharmacogenetically(PGT) guided- versus standard of care(SOC)- warfarin therapy, including the health economics of the two warfarin therapies. An open-label, randomized, controlled clinical trial recruited warfarin-receiving patients in pharmacogenetically(PGT) guided- versus standard of care(SOC)- study arms. Pharmacogenetic analysis of CYP2C9*2(rs1799853), CYP2C9*3(rs1057910) and VKORC1(rs9923231) was performed for patients recruited to the PGT-guided arm. PT(Prothrombin Time)-INR(international normalized ratio) testing and dose titrations were allowed as per routine clinical practice. The primary endpoint was the percent time spent in the therapeutic INR range(TTR) during the 90-day observation period. Secondary endpoints were time to reach therapeutic INR(TRT), the proportion of adverse events, and economic comparison between two modes of therapy in a Markov model built for the commonest warfarin indication- atrial fibrillation. The study enrolled 168 patients, 84 in each arm. Per-protocol analysis showed a significantly high median time spent in therapeutic INR in the genotype-guided arm(42.85%; CI 21.4-66.75) as compared to the SOC arm(8.8%; CI 0-27.2)(p < 0.00001). The TRT was less in the PG-guided warfarin dosing group than the standard-of-care dosing warfarin group (17.85 vs. 33.92 days) (p = 0.002). Bleeding and thromboembolic events were similar in the two study groups. Lifetime expenditure was ₹1,26,830 in the PGT arm compared to ₹1,17,907 in the SOC arm. The QALY gain did not differ in the two groups(3.9 vs. 3.65). Compared to SOC, the incremental cost-utility ratio was ₹35,962 per QALY gain with PGT test opting. In deterministic and probabilistic sensitivity analysis, the base case results were found to be insensitive to the variation in model parameters. In the cost-effectiveness-acceptability curve analysis, a 90% probability of cost-effectiveness was reached at a willingness-to-pay(WTP) of ₹ 71,630 well below one time GDP threshold of WTP used. Clinical efficacy and the cost-effectiveness of the warfarin pharmacogenetic test suggest its routine use as a point of care investigation for patient care in LMICs.

CFD-DEM-based evaluation of main-channel sediment transport processes subject to supplement from a steep tributary

The accumulation of sediments within mountainous gullies serves as a significant source for the main channel. Understanding the transport characteristics is crucial for comprehending reach-scale sediment supply features and channel morphodynamics. This study addresses the previously unexplored challenges in accurately quantifying variations in sediment transport rates, particularly during critical transitions such as the mobilization of static sediment pulses into continuous motion. To accomplish this objective, a combined Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) computational framework is employed to directly simulate the detailed trajectories of all particles. After validating the CFD-DEM model, we examine the effects of three major influential factors (particle size, channel slope and channel width) on sediment transport characteristics. The results indicate that the sediment transport rate exhibits rapid rise to peak followed by gradual attenuation process, described by a long-tail effect. The cross-sectional averaged sediment transport rate positively correlates with the width of sediment transport belt. The downstream propagation of sediment is enhanced by steeper channel slope and narrower channel width. In addition, we find that cross-sectional averaged sediment transport rate controlled by these factors exhibits inherent self-similarity. The temporal variation of dimensionless sediment transport rate for a certain section can be modeled by a modified two-branch Weibull function encompasses three model parameters dependent on influential factors and longitudinal location. The parameter variation of the functional model, combined with linear variation of the three main influencing factors, provides support for explaining sediment transport processes in channels of dimensions different from those studied. These findings enhance the understanding of the formation and evolution mechanism of sediment transport induced by upstream loose deposit destruction.

Cost-Effectiveness Analysis of Innovative Therapies for Patients with Non-Alcoholic Fatty Liver Disease.

Currently there are no disease-specific approved therapies for non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH); however, several treatments are under development. This study aimed to estimate the cost-effectiveness of hypothetical innovative therapies compared with lifestyle intervention alone and combined with pioglitazone, and assess the health economic consequences of their future availability for patients. A Markov cohort model was developed, considering fourteen disease health states and one absorbing state representing death. Transition probabilities, costs, utilities, and treatment efficacy were based on published data and assumptions. Four treatment strategies were considered, including two existing therapies (lifestyle intervention, small molecule treatment) and two hypothetical interventions (biological and curative therapy). The analysis was performed from the US third-party payer perspective. The curative treatment with the assumed efficacy of 70% of patients cured and assumed price of $500,000 was the only cost-effective option. Although it incurred higher costs (a difference of $188,771 vs. lifestyle intervention and $197,702 vs. small molecule), it generated more QALYs (a difference of 1.58 and 1.38 QALYs, respectively), resulting in an ICER below the willingness-to-pay threshold of $150,000 per QALY. The sensitivity analyses showed that the results were robust to variations in model parameters. This study highlighted the potential benefits of therapies aimed at curing a disease rather than stopping its progression. Nonetheless, each of the analyzed therapies could be cost-effective compared with lifestyle intervention at a relatively high price.

Adaptive Neural Network-Based Event-Triggered SOC Observer With Application to a Stochastic Battery Model.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

Analyzing Global Stability of M-Pox Disease Dynamics: Mathematical Insights into Detection and Treatment Strategies

In this research, a mathematical model is constructed to scrutinize the transmission patterns of monkeypox (mpox), with a specific emphasis on integrating the early detection of infected undetected individuals to curb its transmission. This research takes into account a range of factors influencing the propagation of monkeypox, encompassing population demographics, contact dynamics, and the efficacy of early detection of unidentified infected individuals. Employing the next-generation matrix method, the basic reproduction number (R0) is computed, revealing that the disease-free equilibrium state is locally asymptotically stable when (R0&lt;1). This suggests that containment of monkeypox is achievable within a human populace where (R0) remains below one (1), yet it transitions to an endemic state when (R0) exceeds this critical value one (1). Furthermore, a sensitivity analysis is conducted to evaluate the robustness of our findings to variations in model parameters. Utilizing numerical simulations conducted via MAPLE 18, we demonstrate the significance of prompt identification and immediate intervention for infected individuals who may otherwise go undetected, in effectively diminishing the dynamic propagation of monkeypox. The results underscore the pivotal role of early detection in mitigating monkeypox outbreaks and curtailing transmission rates.

The intermediate neutron capture process

Context. The observed surface abundance distributions of carbon-enhanced metal-poor (CEMP) r/s stars suggest that these stars could have been polluted by an intermediate neutron capture process (the so-called i-process) occurring at intermediate neutron densities between the r- and s-processes. Triggered by the ingestion of protons inside a convective He-burning zone, the i-process could be hosted in several sites, a promising one being the early AGB phase of low-mass, low-metallicity stars. The i-process remains affected however by many uncertainties, including those of nuclear origin, since it involves hundreds of nuclei for which reaction rates have not yet been determined experimentally. Aims. We investigate both the systematic and statistical uncertainties associated with theoretical nuclear reaction rates of relevance during the i-process and explore their impact on the i-process elemental production, and subsequently on the surface enrichment, of a low-mass, low-metallicity star during the early AGB phase. Methods. We used the TALYS reaction code to estimate both the model and parameter uncertainties affecting the photon strength function and the nuclear level densities, and hence the radiative neutron capture rates. The impact of correlated systematic uncertainties was estimated by considering different nuclear models, as was detailed in Paper II. In contrast, the uncorrelated uncertainties associated with local variation in model parameters were estimated using a variant of the backward-forward Monte Carlo method to constrain the parameter changes to experimentally known cross sections before propagating them consistently to the neutron capture rates. The STAREVOL code (Siess 2006, A&amp;A, 448, 717) was used to determine the impact of nuclear uncertainties on the i-process nucleosynthesis in a 1 M⊙ [Fe/H] = –2.5 model star during the proton ingestion event in the early AGB phase. A large nuclear network of 1160 species coherently coupled to the transport processes was solved to follow the i-process nucleosynthesis. Results. We find that the uncorrelated parameter uncertainties lead the surface abundance uncertainties of elements with Z ≥ 40 to range between 0.5 and 1.0 dex, with odd-Z elements displaying higher uncertainties. The correlated model uncertainties are of the same order of magnitude, and both model and parameter uncertainties have an important impact on potential observable tracers such as Eu and La. We find around 125 important (n, γ) reactions impacting the surface abundances, including 28 reactions that have a medium to high impact on the surface abundance of elements that are taken as observable tracers of i-process nucleosynthesis in CEMP stars. Conclusions. Both the correlated model and uncorrelated parameter uncertainties need to be estimated coherently before being propagated to astrophysical observables through multi-zone stellar evolution models. Many reactions are found to affect the i-process predictions and will require improved nuclear models guided by experimental constraints. Priority should be given to the reactions influencing the observable tracers.

Rejection of Sinusoidal Disturbances With Unknown Slowly Time-Varying Frequencies for Linear Time-Varying Systems

Abstract Recently, a class of adaptive schemes has been developed for rejecting sinusoidal output disturbances with unknown frequencies, phases, and amplitudes. These adaptive schemes require an accurate model of the affected system's dynamics to function, and the controller performance degrades as the modeling error increases. While linear time-invariant (LTI) dynamics can adequately model the dynamics of some systems, a time-varying model of the system dynamics is more appropriate in certain scenarios. This paper proposes a robust adaptive scheme for rejecting disturbances that affect the output of a system described by a known linear time-varying model. The disturbances are described by an unknown number of sinusoidal terms with unknown time-varying frequencies. Analysis is done in ideal case where the disturbance frequencies are perfectly known to provide conditions guaranteeing the existence of a controller and a corresponding set of parameters which reject the disturbance. Analysis is then done in the more realistic adaptive scenario where the disturbance frequencies are unknown, and a robust adaptive scheme for selecting controller parameters which reject the disturbance is proposed. Performance of the controller with respect to the speed of variation in the plant model parameters is assessed via simulation.

Extended State Kalman Filter-Based Model Predictive Control for Electro-Optical Tracking Systems with Disturbances: Design and Experimental Verification

A modified Extended State Kalman Filter (ESKF)-based Model Predictive Control (MPC) algorithm is introduced to tailor the enhanced disturbance suppression in electro-optical tracking systems. Traditional control techniques, although robust, often struggle in scenarios with concurrent internal, external disturbances, and sensor noise. The proposed algorithm effectively overcomes these limitations by precisely estimating system states and actively mitigating disturbances, thus significantly boosting noise and perturbation control resilience. The primary contributions of this study include the integration of ESKF for accurate system state and disturbance estimation in noisy environments, the embedding of an ESKF estimation-compensation loop to simulate an improved disturbance-free system, and a simplified modeling approach for the controlled device. This designed structure minimizes the reliance on extensive system identification, easing the predictive control model-based constraints. Moreover, the approach incorporates total disturbance estimation into the optimization problem, safeguarding against actuator damage and ensuring high tracking accuracy. Through rigorous simulations and experiments, the ESKF-based MPC has demonstrated enhanced model error tolerance and superior disturbance suppression capabilities. Comparative analyses under varying model parameters and external disturbances highlight its exceptional trajectory tracking performance, even in the presence of model uncertainties and external noise.

A framework for estimating the anthropogenic part of Antarctica’s sea level contribution in a synthetic setting

The relative contributions of anthropogenic climate change and internal variability in sea level rise from the West Antarctic Ice Sheet are yet to be determined. Even the way to address this question is not yet clear, since these two are linked through ice-ocean feedbacks and probed using ice sheet models with substantial uncertainty. Here we demonstrate how their relative contributions can be assessed by simulating the retreat of a synthetic ice sheet setup using an ice sheet model. Using a Bayesian approach, we construct distributions of sea level rise associated with this retreat. We demonstrate that it is necessary to account for both uncertainties arising from both a poorly-constrained model parameter and stochastic variations in climatic forcing, and our distributions of sea level rise include these two. These sources of uncertainty have only previously been considered in isolation. We identify characteristic effects of climate change on sea level rise distributions in this setup, most notably that climate change increases both the median and the weight in tails of distributions. From these findings, we construct metrics quantifying the role of climate change on both past and future sea level rise, suggesting that its attribution is possible even for unstable marine ice sheets.

Nonlinear roll damping coefficients and natural frequency estimation via augmented extended Kalman filtering for floating body

ABSTRACT The nonlinear roll damping coefficients and natural frequency of a floating body are discussed in this article using an augmented Extended Kalman Filtering (EKF) method. This paper presents a non-linear model-based observer for the combined estimation of free roll decay motion states and damping coefficients. The identification framework uses an improved extended Kalman filter (EKF) to deal with model parameter variability and noisy measurement input. The estimated error was compared to the theoretical results obtained from the covariance matrix for filtering correctness. It is found that the nonlinear damping coefficients and natural frequency obtained from the EKF approach are a more realistic representation of the roll dynamics from the general estimation procedure given by the literature. The proposed method also has the built-in capability to remove random noise from the measured roll. The influence of noise levels in the measurements on the identification accuracy was investigated and discussed.