Variation Of Model Parameters Research Articles

Sequential State and Unknown Parameter Estimation Strategy and its Application to a Sensor Fusion Problem

Soft-sensing and state-space-based methods play crucial roles in fault-tolerant sensor fusion schemes. Kalman filtering is one of the most employed sensor fusion methods. In practice, filtering (or state estimation) is sensitive to initial conditions, variations in model parameters, and faults in sensors and actuators. Cumulatively, these uncertainties that affect the state estimator can be modeled as unknown parameters. In this article, we present the development of a discrete-time minimum variance unbiased estimator (MVUE) that is robust to a class of unknown parameters. The estimation gain of MVUE is designed to facilitate unbiased state estimates in the presence of unknown parameters. We then use the innovations that are generated by the proposed state estimator (MVUE) to estimate the unknown parameters, thus proposing a sequential state and unknown parameter estimation strategy. The development of the decoupled state and unknown parameter estimation strategy is motivated by the fault-tolerant sensor fusion exercise for railway track geometry inspection. This exercise aims to formulate the state-space realization of the track geometry inspection problem to enable sequential state and unknown parameter estimation. The track geometry parameters, initial conditions, and sensor faults are considered unknown parameters for the application. The simulation analysis shows the importance of developing the trinity of the state-space realization for track geometry inspection, the state estimation framework, and the decoupled unknown parameter estimator.

Analysis on the pressure dynamic characteristics of slanted well in dual-porosity and dual-permeability reservoir

In order to understand the dual-permeability flow law of slanted well in a fractured reservoir and study the characteristics of pressure dynamics, a flow model of slanted well in a dual-porosity and dual-permeability reservoir is established. The model is solved by Laplace transformation, separation of variables, and superposition of sink source functions, etc. The analytical solution of bottomhole pressure change in Laplace space is obtained. A series of pressure dynamic characteristic curves of slanted well in dual-porosity and dual-permeability reservoirs are programmed and drawn by Matlab software, which are controlled by varying model parameters such as the permeability ratio of fracture system to total system. Based on the pressure dynamic characteristic curves, seven flow regimes are identified, including pure well bore storage effect stage, skin effect stage, early radial-flow, middle linear-flow stage, transitional flow stage, inter-porosity flow stage from matrix to fracture and late radial flow stage. In particular, the differences of flow characteristic curves among slanted well, vertical well and horizontal well are analyzed. It is found that slanted well has more early radial-flow stage and middle linear-flow stage than the vertical well, and these two flow regimes have shorter duration than the horizontal well. In addition, the effect of inclination angle of well, well length and other parameters on the characteristic curve is analyzed. The larger the inclination angle and well length, the lower the curve position, the more obvious the early radial flow stage and the middle linear flow stage. The larger the permeability ratio of fracture system to total system is, the more significant the inter-porosity stage is and the deeper the inter-porosity concave is. The proposed analytical model and a series of pressure dynamic characteristic curves can be used to study the seepage characteristics of slanted wells in fractured reservoirs. <fig fig-type="abstract-image" id="F1" orientation="portrait" position="float"><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="1000-2618-2022-39-6-660/D5477531-C4E9-4093-9DA1-A06C4F43D821-F001.jpg"/></fig>

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fluid–fluid/ fluid–solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance–rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure.

Modeling and Estimation of Self-Phoretic Magnetic Janus Microrobot With Uncontrollable Inputs

This study theoretically investigates the modeling of spherical catalytic self-phoretic magnetic Janus microrobot (MJR) evolving in uniform viscous flow. A 2-D state-space representation of the MJR is developed, exhibiting a state-dependent-coefficient (SDC) form. To evaluate the consistency of the modeling formalism, a dual Kalman filter (DKF) methodology is employed with respect to experimental results when unknown parameters or uncontrollable inputs are considered. In fact, the self-phoretic thrust mechanism and the magnetodynamics of the MJR are not well-known. SDC-DKF is implemented, and we find that there is good agreement between the dynamics computed from our theoretical predictions and the experimental observations in a wide range of model parameter variations.

Privacy-Preserving in-bed pose monitoring: A fusion and reconstruction study

Background and objectives:Recently, in-bed human pose estimation has attracted the interest of researchers due to its relevance to a wide range of healthcare applications. Compared to the general problem of human pose estimation, in-bed pose estimation has several inherent challenges, the most prominent being frequent and severe occlusions caused by bedding. In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings. First, we explore the effective fusion of information from different imaging modalities for better pose estimation. Secondly, we propose a framework that can estimate in-bed pose estimation when visible images are unavailable, and demonstrate the applicability of fusion methods to scenarios where only LWIR images are available. Method:We analyze and demonstrate the effect of fusing features from multiple modalities. For this purpose, we consider four different techniques: (1) Addition, (2) Concatenation, (3) Fusion via learned modal weights, and 4) End-to-end fully trainable approach; with a state-of-the-art pose estimation model. We also evaluate the effect of reconstructing a data-rich modality (i.e., visible modality) from a privacy-preserving modality with data scarcity (i.e., long-wavelength infrared) for in-bed human pose estimation. For reconstruction, we use a conditional generative adversarial network. Results:We conduct experiments on a publicly available dataset for feature fusion and visible image reconstruction. We conduct ablative studies across different design decisions of our framework. This includes selecting features with different levels of granularity, using different fusion techniques, and varying model parameters. Through extensive evaluations, we demonstrate that our method produces on par or better results compared to the state-of-the-art. Conclusion:The insights from this research offer stepping stones towards robust automated privacy-preserving systems that utilize multimodal feature fusion to support the assessment and diagnosis of medical conditions.

Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient boosting machine (LightGBM), a modern machine-learning algorithm, we present a framework for inferring the density-dependent nuclear symmetry energy from observables in heavy-ion collisions on the event-by-event analysis. The ultrarelativistic quantum molecular dynamics (UrQMD) model simulations are used as training data. The symmetry energy slope parameter extracted with LightGBM event-by-event from test data also by UrQMD has an average spread of approximately 30 MeV from the truth, and is found to be robust against variations in model parameters. In addition, LightGBM can identify features that have the greatest effect on the physics of interest, thereby offering valuable insights. Our study suggests that the present framework can be a powerful tool and may offer a new paradigm to study the underlying physics in heavy-ion collisions.

Automated End-Effector Alignment in Robotic Micromanipulation

Proper alignment of the end-effector is a critical procedure that determines the success of micromanipulation, such as robotic cell manipulation. Presently, end-effector alignment is performed manually and suffers from large misalignment error and inconsistency. Manual alignment often undesirably moves the end-effector (e.g., a glass micropipette) out of the limited field of view under microscopy and risks breaking the fragile end-effector. This article presents automated end-effector alignment in robotic micromanipulation. A rotational degree of freedom was added to a standard micromanipulator with translational degrees of freedom. The kinematic model of end-effector’s rotation was established, and the unknown model parameters were calibrated. To accommodate model uncertainty and parameter variations, a sliding mode controller was designed to achieve end-effector alignment. Experimental results demonstrate that the robotic alignment technique achieved an accuracy of 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 0.3^{\circ }$</tex-math></inline-formula> and a time cost of 17.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 7.3 s, both significantly less than manual alignment. The developed controller based on kinematic modeling and sliding mode control achieved a higher success rate and significantly less time cost for end-effector alignment than the PID controller. Standard micropipettes were used as the end-effectors for sperm immobilization and oocyte penetration, important procedures in cell surgeries. The success rate of sperm immobilization was 98% by robotic micropipette alignment, higher than the success rate of 90% by manual alignment. Oocyte deformation before penetration was 28.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 7.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m by robotic end-effector alignment, significantly less than the deformation of 54.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 13.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m by manual alignment.

Robust Predictive Control for Linear Permanent Magnet Synchronous Motor Drives Based on an Augmented Internal Model Disturbance Observer

To optimize the current-control performance of the linear permanent magnet synchronous motor (LPMSM) drive system, a robust continuous time model-based predictive control for the LPMSM drive systems is proposed in this article. Since the current regulation technology is fundamental for obtaining the precise thrust of the LPMSM, a continuous time model-based predictive control is utilized in the current loop to achieve a fast dynamic response and accurate tracking accuracy. However, the predictive control is a model-based scheme, thus, the model uncertainties and parameter variations will influence the accuracy of stator current. To solve this problem, an augmented internal model disturbance observer is proposed to estimate the lumped disturbances and then compensate to the forward channel. First, the internal model equation of the current loop is constructed by the internal model control principle. Then, a new augmented matrix is constructed by introducing the error variables of the actual current and the internal model current. The observer control law is designed by the state feedback theory, and the observer parameters are optimized by pole assignment method. Finally, the experimental results demonstrate the correctness and effectiveness of the proposed method.

Motion Analysis and Real‐Time Trajectory Prediction of Magnetically Steerable Catalytic Janus Micromotors

Chemically driven micromotors display unpredictable trajectories due to the rotational Brownian motion interacting with the surrounding fluid molecules. This hampers the practical applications of these tiny robots, particularly where precise control is a requisite. To overcome the rotational Brownian motion and increase motion directionality, robots are often decorated with a magnetic composition and guided by an external magnetic field. However, despite the straightforward method, explicit analysis and modeling of their motion have been limited. Here, catalytic Janus micromotors are fabricated with distinct magnetizations and a controlled self‐propelled motion with magnetic steering is shown. To analyze their dynamic behavior, a dynamic model that can successfully predict the trajectory of micromotors in uniform viscous flows in real time by incorporating a form of state‐dependent‐coefficient with a robust two‐stage Kalman filter is theoretically developed. A good agreement is observed between the theoretically predicted dynamics and experimental observations over a wide range of model parameter variations. The developed model can be universally adopted to various designs of catalytic micro‐/nanomotors with different sizes, geometries, and materials, even in diverse fuel solutions. Finally, the proposed model can be used as a platform for biosensing, detecting fuel concentration, or determining small‐scale motors’ propulsion mechanisms in an unknown environment.

STRUCTURAL BREAKS DAN KETIDAKSTABILAN PERMINTAAN UANG DI INDONESIA

This research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The results show that the money demand function (M1 dan M2) is not cointegrated (unstable) and the source of the instability is exchange rate variable.

Sensitivity of muscle force response of a two-state cross-bridge model to variations in model parameters.

Muscle models based on the cross-bridge theory (Huxley-type models) are frequently used to calculate muscle forces for different contractile conditions. Dynamic and nonlinear characteristics of muscle forces produced during isometric, concentric, and eccentric contractions can be represented to a limited extent by using cross-bridge models. Cross-bridge models use various parameters to simulate force responses. However, there remains uncertainty as to the effect of changes in model parameters on force responses in Huxley-type models. In this study, we aimed to analyze the sensitivity of force response to changes in model parameters in Huxley-type models. A two-state Huxley model was used to determine the cross-bridge attachment distributions and forces for shortening and lengthening contractions. Sensitivity of muscle force to changes in attachment rate, detachment rate, and cross-bridge binding distance was examined within a range of ±20% of the nominal value using Monte Carlo simulations. Changes in the detachment rate influenced the predicted muscle forces the most for lengthening contractions, while changes in attachment rate and binding distance affected forces the most for shortening contractions. These results show once more the asymmetry between shortening and lengthening contractions and the difficulty in using a single cross-bridge model to predict forces during shortening and elongation accurately.

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin.

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

Species abundance correlations carry limited information about microbial network interactions.

Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation.

Differential inversion using surface wave methods for time-lapse monitoring

Climate-changing, human activities or aging problems create variations in civil engineering structures and materials which, if monitored, bring useful information for maintenance. Time-lapse (TL) monitoring measures the present condition of a given material, named the baseline, and then repeat a successive series of measurements at different times, named repeatlines. By comparing the baseline with one of the repeatlines, material properties’ variations, including weak ones, can be inferred. Surface wave (SW) methods are widely used for TL monitoring, because SW are very energetic and the inversion of their dispersion properties gives a depth-profile of some mechanical parameters. Classical SW inversion uses SW phase velocity (Vph) as inversion input data. It suffers from low sensitivity to the deep medium’s parameters due to high uncertainties in the measurements at low frequencies. TL monitoring can partially overcome the difficulties linked to the estimation of deep variations. In this study, we propose a new strategy for TL monitoring using SW methods, by looking for the relation between the model parameter variations and the data difference within the frame of global inverse problems. For this purpose, we introduce differential inversion (DI) in surface wave methods. Instead of independent inversions of the baseline and repeatlines, DI uses the difference between measured data of baseline and repeatlines as inversion input data. Two approaches have been tested. The first approach uses an innovative method to calculate the data difference, which is called the diagram difference (DD). DD is based on a statistical distance considering the area of interest of the dispersion diagram as a histogram of Vph distribution. This DI is tested experimentally on laboratory-measured data, obtained on three mortar-concrete slabs. The mortar layers of the three slabs have different water-to-cement ratios which change the mechanical properties of each slab. DI shows more concentrated results closer to the true values for both layers compared to the SW inversion using Vph as inversion input data. The second approach uses a linear approximation of Rayleigh wave Vph in the inversion process, in order to relates the data difference with the model parameters variations by using the analytical equation of the sensitivity kernel. Numerical tests show that this approach is limited to model parameter variations less than 5%, beyond which the linear assumption of Rayleigh wave Vph is no longer valid. This approach is tested on three epoxy-resin models, with respect to the 5% variation limitation in the deep layer. The low cost of the computation time is the main feature of this second approach. Its main drawback is that it requires a good estimation of the baseline model in order to correctly calculate the sensitivity kernel. In this case, the first DI approach can be used for a prior estimate of the medium’s variations, since it has a better resolution on the inversion results compared to SW Vph inversion.

Modeling microtubule dynamic instability: Microtubule growth, shortening and pause

Microtubules (MTs) are protein polymers found in all eukaryotic cells. They are crucial for normal cell development, providing structural support for the cell and aiding in the transportation of proteins and organelles. In order to perform these functions, MTs go through periods of relatively slow polymerization (growth) and very fast depolymerization (shortening), where the switch from growth to shortening is called a catastrophe and the switch from shortening to growth is called a rescue. Although MT dynamic instability has traditionally been described solely in terms of growth and shortening, MTs have been shown to pause for extended periods of time, however the reason for pausing is not well understood. Here, we present a new mathematical model to describe MT dynamics in terms of growth, shortening, and pausing. Typically, MT dynamics are defined by four key parameters which include the MT growth rate, shortening rate, frequency of catastrophe, and the frequency of rescue. We derive a mathematical expression for the catastrophe frequency in the presence of pausing, as well as expressions to describe the total time that MTs spend in a state of growth and pause. In addition to exploring MT dynamics in a control-like setting, we explore the implicit effect of stabilizing MT associated proteins (MAPs) and stabilizing and destabilizing chemotherapeutic drugs that target MTs on MT dynamics through variations in model parameters.

A New Flexible Extension of Xgamma Distribution and its Application to COVID-19 Data

In this article, a new flexible extension of xgamma probability distribution has been proposed. Several well known distributional properties viz., raw moments, generating functions, conditional moments, mean deviation, quantile functions etc., of this flexible extension model have derived and studied in detail. Further, the estimation of the unknown model parameters along with the survival function and hazard function are estimated using maximum likelihood estimation technique. The Monte Carlo simulation has been performed to check the consistency of the proposed estimators for the different variation of sample size and model parameters. Finally, the superiority of proposed extension over several well known lifetime models has been illustrated using four data sets pertaining to COVID-19 cases in different country of the world.

Running vacuum versus holographic dark energy: a cosmographic comparison

We perform a comparative study of different types of dynamical dark energy models (DDEs) using the cosmographic method. Among the models being examined herein we have the Running Vacuum models (RVMs), which have demonstrated considerable ability to fit the overall cosmological data at a level comparable to the standard cosmological model, Lambda CDM, and capable of alleviating the sigma _8 and H_0 tensions. At the same time we address a variety of Holographic dark energy models (HDEs) with different options for the time (redshift)-varying model parameter c=c(z). We deal with the HDEs under the double assumption of fixed and evolving holographic length scale and assess which one is better. Both types of DDEs (RVMs and HDEs) are confronted with the most robust cosmographic data available, namely the Pantheon sample of supernovae data (SnIa), the baryonic acoustic oscillations data (BAOs) extracted from measurement of the power spectrum and bispectrum of the BOSS data release, and the cosmic chronometer measurements of the Hubble rate (CCHs) at different redshifts obtained from spectroscopic observations of passively evolving galaxies. Using these data samples we assess the viability of the mentioned DDEs and compare them with the concordance Lambda CDM model. From our cosmographic analysis we conclude that the RVMs fare comparably well to the Lambda CDM, a fact which adds up more credit to their sound phenomenological status. In contrast, while some of the HDEs are favored using the current Hubble horizon as fixed holographic length, they become highly unfavoured in the more realistic case when the holographic length is dynamical and evolves as the Hubble horizon.

A novel machine learning pipeline to detect malicious anomalies for the Internet of Things

Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices. There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail.In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Combined Semi-mechanistic Target-Mediated Drug Disposition and Pharmacokinetic-Pharmacodynamic Models of Alirocumab, PCSK9, and Low-Density Lipoprotein Cholesterol in a Pooled Analysis of Randomized Phase I/II/III Studies.

Alirocumab is a cholesterol-lowering monoclonal antibody targeting proprotein convertase subtilisin kexin type 9 (PCSK9) indicated in the prevention of cardiovascular risk and exhibiting target-mediated drug disposition (TMDD). The aim of this work was to develop an integrated pharmacokinetic-pharmacodynamic model to describe the interaction of alirocumab with PCSK9 and its impact on the evolution of low-density lipoprotein cholesterol (LDL-C) levels and explore labeling specification for subpopulations. Using data collected from nine phase I/II/III clinical studies (n=527, subcutaneous or intravenous administration), a TMDD model considering the quasi-steady-state approximation was developed to characterize the interaction dynamics of alirocumab and PCSK9, combined with an indirect pharmacodynamic model describing the inhibition of LDL-C by PCSK9 in a one-step approach using nonlinear-mixed effects modeling. A "full fixed effects modeling" strategy was implemented to quantify parameter-covariate relationships. The model captures the interaction between alirocumab and its target PCSK9 and how this mechanism drives LDL-C depletion, with an estimation of the associated between-subject variability of model parameters and the quantification of clinically relevant parameter-covariate relationships. Co-administration of statins was found to increase the central volume of distribution of alirocumab by 1.75-fold (5.6L versus 3.2L) and allow for a 14% greater maximum lipid-lowering effect (88% versus 74%), highlighting the synergy of action between anti-PCSK9 therapeutic antibodies and statins toward lowering LDL-C plasma levels. Baseline levels of PCSK9 were found to be related to the amplitude of LDL-C variations by increasing the concentration of free PCSK9 necessary to reach half its capacity of inhibition of LDL-C degradation. The maximum effect of alirocumab is achieved when free PCSK9 concentration is close to zero, as seen mostly after 150mg every 2weeks (Q2W) or 300 mg every 4weeks (Q4W), indicating that there would be no additional clinical benefit of increasing the dose higher than these recommended dosing regimens.