A computer program for enzyme kinetics that combines model discrimination, parameter refinement and sequential experimental design.

This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI) sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED) technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment. Sequential BED, as opposed to static BED, considers the scenario where we can sequentially update our beliefs about the model parameters through data gathered in the experiment. A class of models of particular interest for the natural and medical sciences are implicit models, where the data generating distribution is intractable, but sampling from it is possible. Even though there has been a lot of work on static BED for implicit models in the past few years, the notoriously difficult problem of sequential BED for implicit models has barely been touched upon. We address this gap in the literature by devising a novel sequential design framework for parameter estimation that uses the Mutual Information (MI) between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models. Our approach uses likelihood-free inference by ratio estimation to simultaneously estimate posterior distributions and the MI. During the sequential BED procedure we utilise Bayesian optimisation to help us optimise the MI utility. We find that our framework is efficient for the various implicit models tested, yielding accurate parameter estimates after only a few iterations.

Data assimilation is predominantly used for state estimation, combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts, we need good estimates of both the current system state and the model parameters. This article presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three‐dimensional variational (3D‐Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from synthetic aperture radar (SAR) satellite imagery and a ship‐borne survey is shown to significantly improve the predictive capability of the model over a 2‐year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this article considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D‐Var data assimilation algorithm for joint state‐parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation‐based state‐parameter estimation in coastal morphodynamic modelling. Copyright © 2012 Royal Meteorological Society

"Line, Maurice Bernard, (21 June 1928–21 Sept. 2010), Director General (Science, Technology and Industry), British Library, 1985–88 (Deputy Director General, 1973–74, Director General, 1974–85, Lending Division)" published on by Oxford University Press.

This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first‐order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall \(\tau \) estimation method for copulas parameter estimation. The methods were employed to study joint severity–duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The \(Q_{75}\) index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma–GEV, LN2–exponential, and LN2–gamma were selected as the best paired drought severity–duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov–Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-\(\tau \) is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall \(\tau \) estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.

Experimental design for model discrimination of thermodynamic models

A program has been developed that will produce simulated enzyme kinetic data suitable for inclusion in problem sets for undergraduates. Mechanisms simulated include one- and two-substrate reactions and various types of inhibition. The effects of variation of pH and temperature may be modeled, as may the effects of random errors on the experimental procedure. The graphical output is suitable for use in lecture demonstrations.

Conducting new experiments for biomass gasifiers is expensive and time consuming. Therefore, it is important to select conditions for new experiments so maximum information is obtained. In this study, sequential Bayesian model-based design of experiments (MBDoE) is used to design new experimental runs for a heat-integrated biomass downdraft gasifier. This MBDoE approach is valuable because it accounts for model structure, prior information about plausible parameter values, and previous experimental data in a relatively simple way. Operating conditions selected for each new run are biomass moisture content, water injection rate, and the desired energy demand from the downstream engine. Three types of MBDoE with different objective functions are considered: A-optimal, V-optimal, and a proposed new type of focused V-optimal design. A-optimal design is used to when the goal is to obtain improved parameter estimates, without specifying how the model will be used. Performing two new A-optimal runs reduced the standard deviations for model parameters by 18.4% on average compared to when only old data are available. The three most-improved parameter estimates are activation energies for char gasification reactions involving carbon dioxide, hydrogen, and steam, respectively. The focused V-optimal methodology results in greater improvements in prediction accuracy for tar concentration and outlet temperature, which are key model responses. Using two designed Vf-optimal runs reduces standard deviations for these variables by 59.4%, on average, compared to when only old data are available. New A-optimal and V-optimal runs lead to corresponding improvements of 30.7% and 50%, respectively.

Exploiting the information content offered by geoelectric data in an efficient manner requires careful selection of the electrode configurations to be used. This can be achieved using sequential experimental design techniques proposed over the past few years. However, these techniques become impractical when large-scale 2D or 3D experiments have to be designed. Even if sequential experimental design were applicable, acquisition of the resulting data sets would require an unreasonably large effort using traditional multielectrode arrays. We present a new, fully parallelized pole-bipole measuring strategy by which large amounts of data can be acquired swiftly. Furthermore, we introduce a new experimental design concept that is based on “complete” data sets in terms of linear independence. Complete data sets include a relatively small number of basis electrode configurations, from which any other configuration can be reconstructedby superposition. The totality of possible configurations is referred to as the comprehensive data set. We demonstrate the benefits of such reconstructions using eigenvalue analyses for the case of noise-free data. In the presence of realistic noise, such reconstructions lead to unstable results when only four-point (bipole-bipole) configurations are considered. In contrast, complete three-point (pole-bipole) data sets allow more stable reconstructions. Moreover, complete pole-bipole data sets can be acquired very efficiently with a fully parallelized system. Resolution properties of complete pole-bipole data sets are illustrated using both noise-free and noisy synthetic data sets. We also show results from a field survey performed over a buried waste disposal site, which further demonstrates the usefulness of our approach. Although this paper is restricted to 2D examples, it is trivial to extend the concept to 3D surveys, where the advantages of parallelized pole-bipole data acquisition become very significant.

The presence of bovine serum albumin (BSA) largely modulates the enzyme kinetics parameters of the human UDP-glucuronosyltransferase (UGT) 1A9, increasing both the apparent aglycone substrate affinity of the enzyme and its limiting reaction velocity (Drug Metab Dispos 39:2117-2129, 2011). For a better understanding of the BSA effects and an examination of whether its presence changes the catalytic mechanism, we have studied the enzyme kinetics of 4-methylumbelliferone glucuronidation by UGT1A9 in the presence and absence of 0.1% BSA, using bisubstrate enzyme kinetic experiments, in both the forward and reverse directions, as well as product and dead-end inhibition. The combined results strongly suggest that the reaction mechanism of UGT1A9, and presumably other human UGTs as well, involves the formation of a compulsory-order ternary-complex, with UDP-α-d-glucuronic acid (UDPGA) as the first binding substrate. Based on the enzyme kinetic parameters measured for the forward and reverse reactions, the equilibrium constant of the overall reaction was calculated (Keq = 574) and the relative magnitudes of the reaction rate constants were elucidated. The inclusion of BSA in the bisubstrate kinetic experiments quantitatively changed the apparent enzyme kinetic parameters, presumably by removing internal inhibitors that bind to the binary enzyme-UDPGA (E-UDPGA) complex, as well as to the ternary E-UDPGA-aglycone complex. Nevertheless, the underlying compulsory-order ternary-complex mechanism with UDPGA binding first is the same in both the absence and presence of BSA. The results offer a novel understanding of UGT enzyme kinetic mechanism and BSA effects.

The objectives of this study were to: (i) examine the efficacy of two sampling techniques for characterizing solute transport under steady‐state water flow, (ii) study the variation in transport model parameters with increasing depth of solute leaching, and (iii) perform model discrimination to examine the transport process operative within a field plot. Bromide, NO ‐ 3 , and Cl ‐ were applied sequentially to a plot instrumented with two sets of 12 solution samplers located at depths of 0.25 and 0.65 m. At the conclusion of the experiment we destructively sampled the entire 2.0 by 2.0 m plot to a depth of 2.0 m. Mass recovery by the solution samplers ranged from 63 to 83% for the three tracers, and recovery by soil excavation ranged from 96 to 105%. The mean solute velocity estimated with the solution sampler data was significantly less than that determined by soil excavation. Mean solute velocity determined from soil excavation implied an effective transport volume equal to 0.829, (where 0, is volumetric water content) for the three tracers. Solution samplers and soil excavation provided similar measures of vertical dispersion. Both sampling methods revealed a scale‐dependent dispersion process in which the dispersivity increased linearly with mean residence time. The depth profiles for all three solutes were accurately described with a stochastic convective lognormal transfer function model (CLT) using the applied mass and two constant parameters (estimated from simultaneous fitting to the depth profiles).

Model Predictive Control (MPC) is a solution towards more energy-efficient waste treatment without compromising on treatment quality. A key component is the process model describing how the inputs and outputs correlate. MPC uses this model to predict future outputs over a finite horizon to decide on step changes to make at the input. These step changes are made so that the output reaches and maintains at a user specified set point. For MPC to be effective, the process model needs to accurately describe the process behaviour. This is a difficult challenge in waste treatment processes due to a combination of slow response, process complexity, and large disturbances. This research project investigated two research avenues towards developing better modelling techniques. This would result in more accurate models or achieve a sufficiently accurate model with fewer experiments. The first avenue is Constrained Model Identification (CMI). Model identification is an optimisation problem to estimate the model parameters. In CMI, process knowledge from first principles and operator experience is translated into optimisation constraints to aid data-driven model identification. The second avenue is Sequential Optimal Experiment Design (SOED). This uses the concept of measuring a value representing information content of a dataset. Like MPC, SOED uses the model to make output predictions. The expected output response to a sequence of input steps form a dataset, and SOED is an optimisation problem to maximise the information content of that expected dataset, by changing the input step sequence. Once optimised, this step sequence is applied in the next experiment. The third part of this work focused on farm-fed anaerobic digestion. It is a renewable energy technology fuelled by agricultural waste. They rely on government incentives to be profitable, but these incentives have steadily been decreased. This project investigated methods to help farmers in the day-to day operation of the unit, including biogas production estimation, automated fault identification and partial diagnosis.

Early in 1980, a survey was made to determine the degree of overlap between each of seven major lists of serials used in checking requests for serial titles not held by the British Library Lending Division. Previously reported surveys have examined what happens to serial requests satisfied outside the Lending Division, but have not distinguished between requests for which the Lending Division holds other issues of the serial in question and requests for titles the Lending Division does not hold at all. The majority of requests not satisfied from Lending Division stock are in the former category. However, failures due to absence of titles require a similar number of staff, because the likelihood of finding a location is less in such cases, and because more work is required to verify the titles.

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines.