Variation Of Model Parameters Research Articles

Technical variability of cornea parameters derived from anterior segment OCT fitted with Fringe Zernike polynomials.

This study uses bootstrapping to evaluate the technical variability (in terms of model parameter variation) of Zernike corneal surface fit parameters based on Casia2 biometric data. Using a dataset containing N = 6953 Casia2 biometric measurements from a cataractous population, a Fringe Zernike polynomial surface of radial degree 10 (36 components) was fitted to the height data. The fit error (height - reconstruction) was bootstrapped 100 times after normalisation. After reversal of normalisation, the bootstrapped fit errors were added to the reconstructed height, and characteristic surface parameters (flat/steep axis, radii, and asphericities in both axes) extracted. The median parameters refer to a robust surface representation for later estimates of elevation, whereas the SD of the 100 bootstraps refers to the variability of the surface fit. Bootstrapping gave median radius and asphericity values of 7.74/7.68 mm and -0.20/-0.24 for the corneal front surface in the flat/steep meridian and 6.52/6.37 mm and -0.22/-0.31 for the corneal back surface. The respective SD values for the 100 bootstraps were 0.0032/0.0028 mm and 0.0093/0.0082 for the front and 0.0126/0.0115 mm and 0.0366/0.0312 for the back surface. The uncertainties for the back surface are systematically larger as compared to the uncertainties of the front surface. As measured with the Casia2 tomographer, the fit parameters for the corneal back surface exhibit a larger degree of variability compared with those for the front surface. Further studies are needed to show whether these uncertainties are representative for the situation where actual repeat measurements are possible.

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology typically rely on a single cost function computed on long-term streamflow series. Even when hydrological models achieve acceptable scores in NSE and KGE, imbalances can still arise between overall model performance and its ability to simulate flood events, particularly flash floods. Multi-scale signatures, which refer to hydrological signatures computed at different temporal and/or spatial scales, and distributed flood modeling, which accounts for spatial variability in input variables and model parameters, are important concepts in hydrological modeling. In this study, the potential of using multi-scale signatures is explored to enhance multi-criteria calibration methods for spatially distributed flood modeling, which remains considerable challenges. We present a novel signatures and sensitivity-based calibration approach implemented into a variational data assimilation algorithm capable to deal with high dimensional spatially distributed hydrological optimization problems. It is tested on 141 flash flood pronecatchments mostly located in the French Mediterranean region. Our approach involves computing several signatures, including flood event signatures, using an automated flood segmentation algorithm. We select suitable signatures for constraining the model based on their global sensitivity with the input parameters through global signature-based sensitivity analysis (GSSA). We then perform two multi-criteria calibration strategies using the selected signatures, including a single-objective optimization approach, which transforms the multi-criteria problem into a single-objective function, and a multi-objective optimization approach, which uses a simple additive weighting method to select an optimal solution from the Pareto set. Our results show significant improvements in both calibration and temporal validation metrics, especially for flood signatures, demonstrating the robustness and delicacy of our signatures-based calibration framework for enhancing flash flood forecasting systems.

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

This paper presents an alternative method for monitoring groundwater levels and estimating specific yields of an unconfined aquifer under different seasonal conditions. The approach employs the Time-Lapse Electrical Resistivity Imaging (TL-ERI) method and machine learning-based time series clustering. A TL-ERI survey was conducted at ten sites (WS01-WS10 sites) throughout the dry and wet seasons, with five-time measurements collected for each site, in the Taichung-Nantou Basin along the Wu River, Central Taiwan. The obtained resistivity raw data was inverted and converted into normalized water content values using Archie’s law, followed by applying the Van Genuchten (VG) model for the Soil Water Characteristic Curve to estimate the Groundwater Level (GWL), and estimated the theoretical specific yield (Sy) by computing the difference between the saturated and residual water contents of the fitted VG model. In addition, the specific yield capacity (Sc), representing the nature of the storage capacity in the aquifer, was also calculated. The results showed that this approach was able to estimate those hydrogeological parameters. The spatial distribution of the GWL reveals that during the dry-wet seasons from February to July, there was a high GWL that extended from southeast to northwest. Conversely, during the wet-dry seasons from July to October, the high GWL shrank, which can be attributed to recharge variations from rainfall events. The determined spatial distribution of Sy and Sc fall within the range of 0.03–0.24 and 0.14–0.25, respectively. To quantitatively establish areas of similar groundwater level changes along with the VG model parameter variations during the study period, a Time series Clustering analysis (TSC) was performed by utilizing Hierarchical Agglomerative Clustering (HAC). The findings suggest that the WS03 site is a promising area for further investigation due to its highest Sc value with a slight change in groundwater levels during the dry and wet seasons. This study brings an advanced development of the geoelectrical method to estimate regional hydrogeological parameters in an area with limited available groundwater observation wells, in different seasonal conditions for groundwater management purposes.

Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference

Abstract. Ground-motion correlation models play a crucial role in regional seismic risk modeling of spatially distributed built infrastructure. Such models predict the correlation between ground-motion amplitudes at pairs of sites, typically as a function of their spatial proximity. Data from physics-based simulators and event-to-event variability in empirically derived model parameters suggest that spatial correlation is additionally affected by path and site effects. Yet, identifying these effects has been difficult due to scarce data and a lack of modeling and assessment approaches to consider more complex correlation predictions. To address this gap, we propose a novel correlation model that accounts for path and site effects via a modified functional form. To quantify the estimation uncertainty, we perform Bayesian inference for model parameter estimation. The derived model outperforms traditional isotropic models in terms of the predictive accuracy for training and testing data sets. We show that the previously found event-to-event variability in model parameters may be explained by the lack of accounting for path and site effects. Finally, we examine implications of the newly proposed model for regional seismic risk simulations.

Robust [formula omitted] parameterization with low tuning complexity of cascaded control for feed drives

This paper presents a robust parameterization method for the well-established cascaded control structure of feed drives and applies it to both proportional (P) and sliding mode (SMC) position controllers. The controller parameterization is based on the structured μ-synthesis technique, which allows direct robust tuning of fixed control structures with respect to the conflicting goals of tracking and disturbance rejection over different frequency ranges. The proposed strategy explicitly takes into account both matched and unmatched uncertainties and considers the dynamics of the Kalman filter and feedforward control, ensuring robust stability and performance based on a simple low-order plant model with only two parameters. To ensure industrial applicability, the parameterization problem is formulated in a signal-based manner and the tuning complexity is limited to the selection of only two hyperparameters by directly extracting the information from measurements. Extensive experimental results are performed on an industrial feed drive to validate the performance and robustness of the proposed approach. For example, compared to P-PI cascade control with feedforward and nominally tuned SMC position control, the robustly parameterized SMC position controller reduces the root-mean-square tracking error by 79% and 61% during high-speed milling experiment. Even under variation of model parameters by 20%, the robustly parameterized SMC still cuts the tracking error by more than 44% in the comparison.

COVID-19-associated pulmonary aspergillosis in immunocompetent patients: a virtual patient cohort study.

The opportunistic fungus Aspergillus fumigatus infects the lungs of immunocompromised hosts, including patients undergoing chemotherapy or organ transplantation. More recently however, immunocompetent patients with severe SARS-CoV2 have been reported to be affected by COVID-19 Associated Pulmonary Aspergillosis (CAPA), in the absence of the conventional risk factors for invasive aspergillosis. This paper explores the hypothesis that contributing causes are the destruction of the lung epithelium permitting colonization by opportunistic pathogens. At the same time, the exhaustion of the immune system, characterized by cytokine storms, apoptosis, and depletion of leukocytes may hinder the response to A. fumigatus infection. The combination of these factors may explain the onset of invasive aspergillosis in immunocompetent patients. We used a previously published computational model of the innate immune response to infection with Aspergillus fumigatus. Variation of model parameters was used to create a virtual patient population. A simulation study of this virtual patient population to test potential causes for co-infection in immunocompetent patients. The two most important factors determining the likelihood of CAPA were the inherent virulence of the fungus and the effectiveness of the neutrophil population, as measured by granule half-life and ability to kill fungal cells. Varying these parameters across the virtual patient population generated a realistic distribution of CAPA phenotypes observed in the literature. Computational models are an effective tool for hypothesis generation. Varying model parameters can be used to create a virtual patient population for identifying candidate mechanisms for phenomena observed in actual patient populations.

On the Ca2＋ elevation in vascular endothelial cells due to inositol trisphosphate-sensitive store receptors activation: A data-driven modeling approach

Agonist-induced Ca2+ signaling is essential for the regulation of many vital functions in endothelial cells (ECs). A broad range of stimuli elevate the cytosolic Ca2＋ concentration by promoting a pathway mediated by inositol 1,4,5 trisphosphate (IP3) which causes Ca2＋ release from intracellular stores. Despite its importance, there are very few studies focusing on the quantification of such dynamics in the vascular endothelium. Here, by using data from isolated ECs, we established a minimalistic modeling framework able to quantitatively capture the main features (averaged over a cell population) of the cytosolic Ca2＋ response to different IP3 stimulation levels. A suitable description of Ca2＋-regulatory function of inositol 1,4,5 trisphosphate receptors (IP3Rs) and corresponding parameter space are identified by comparing the different model variants against experimental mean population data. The same approach is used to numerically assess the relevance of cytosolic Ca2＋ buffering, as well as Ca2＋ store IP3-sensitivity in the overall cell dynamics. The variability in the dynamics’ features observed across the population can be explained (at least in part) through variation of certain model parameters (such as buffering capacity or Ca2＋ store sensitivity to IP3). The results, in terms of experimental fitting and validation, support the proposed minimalistic model as a reference framework for the quantification of the EC Ca2＋ dynamics induced by IP3Rs activation.

Predicting the net administered activity in 90 Y-radioembolization patients from post-procedure 90 Y-SPECT/CT.

The calculation of the net administered activity (Aadmin ) in patients undergoing 90 Y-radioembolization is essential for dosimetry and radiation safety, yet current methods for measuring residual 90 Y activity are often associated with high uncertainty. Therefore, an accurate, robust, and clinically viable method for the determination of Aadmin across approved 90 Y microsphere devices is desirable. We report on a novel method to determine Aadmin by leveraging the quantitative capabilities of SPECT/CT to measure 90 Y-emission in vivo from patients following 90 Y-radioembolization with glass or resin microspheres. 90 Y-SPECT/CT attenuation-corrected count data from 147 sequential 90 Y-radioembolization patients was used for this analysis. Aadmin was calculated as part of routine clinical practice via the exposure rate differences between the initial 90 Y-vial and the 90 Y-residual jar. This served as our gold standard measure of Aadmin . Patient data for each microsphere device were separated into training and testing cohorts to first develop regression models and then to independently assess model performance. The training cohorts were divided into four groups: first, based on the microsphere device (glass or resin), and second, based on the SPECT volume used to calculate counts (the full SPECT field of view (FOV) or liver only (VOIliver )). Univariate linear regression models were generated for each group to predict Aadmin based on 90 Y-SPECT data from the training cohorts. Leave-one-out cross validation was implemented to estimate variability in model parameters. To assess performance, linear models derived from the training cohort were applied to 90 Y-SPECT data from the testing cohort. A comparison of the models between microspheres devices was also performed. Linear models derived from the glass and resin training cohorts demonstrated a strong, positive correlation between 90 Y-SPECT image counts and Aadmin for VOIliver and FOV with R2 >0.98 in all cases. In the glass training cohort, model accuracy (100%-absolute mean prediction error) and precision (95% prediction intervals of mean prediction error) were 99.0% and 15.4% for the VOIliver and 99.7% and 17.5% for the FOV models, respectively. In the resin training cohort, the corresponding values were 98.6% and 16.7% for VOIliver and>99.9% and 11.4% for the FOV models, respectively. The application of these linear models to 90 Y-SPECT data from the testing cohort showed Aadmin prediction errors to have high accuracy and precision for both microsphere devices. For the glass testing cohort, accuracy (precision) was 96.9% (19.6%) and 98.8% (21.1%) for the VOIliver and FOV models, respectively. The corresponding values for the resin training cohort were 97.3% (26.2%) and 98.5% (25.7%) for the VOIliver and FOV models, respectively. The slope of the linear models between the two microsphere devices was observed to be significantly different with resin microspheres generating 48%-49% more SPECT counts for equivalent 90 Y activity based on each device manufacturer's activity calibration process. 90 Y-SPECT image counts can reliably predict (accuracy>95% and precision<18%) Aadmin after 90 Y-radioembolization, with performance characteristics essentially equivalent for both glass and resin microspheres. There is a clear indication that activity calibrations are fundamentally different between the two microsphere devices.

GENERATIVE MODELS FOR LARGE-SCALE SIMULATIONS OF CONNECTOME DEVELOPMENT.

Functional interactions and anatomic connections between brain regions form the connectome. Its mathematical representation in terms of a graph reflects the inherent neuroanatomical organization into structures and regions (nodes) that are interconnected through neural fiber tracts and/or interact functionally (edges). Without knowledge of the ground truth topology of the connectome, functional (directional or nondirectional) graphs represent estimates of signal correlations, from which underlying mechanisms and processes, such as development and aging, or neuropathologies, are difficult to unravel. Biologically meaningful simulations using synthetic graphs with controllable parameters can complement real data analyses and provide critical insights into mechanisms underlying the organization of the connectome. Generative models can be highly valuable tools for creating large datasets of synthetic graphs with known topological characteristics. However, for these graphs to be meaningful, the variation of model parameters needs to be driven by real data. This paper presents a novel, data-driven approach for tuning the parameters of the generative Lancichinetti-Fortunato-Radicchi (LFR) model, using a large dataset of connectomes (n = 5566) estimated from resting-state fMRI from early adolescents in the historically large Adolescent Brain Cognitive Development Study (ABCD). It also presents an application, i.e., simulations using the LFR, to generate large datasets of synthetic graphs representing brains at different stages of neural maturation, and gain insights into developmental changes in their topological organization.

Study on the Selection Method of Federated Learning Clients for Smart Manufacturing

Artificial intelligence technology in the context of smart manufacturing uses manufacturing data to enable the automatic detection, classification, and identification of products in the production process, reducing production costs and human consumption, thereby improving production efficiency and product quality. Federated learning enables the distributed implementation of AI technologies, keeping data local to avoid privacy leaks. However, data heterogeneity factors have an impact on federated learning in a manufacturing context, and this paper proposes a customer degree selection method based on model parameter variation. The method relies on transmitting the local model changes in the participants to reflect the data characteristics, calculates the model similarity of the participants using graph theory and similarity, and uses the Top-K mechanism to filter the original participant set through the similarity scores of graph nodes to reduce the influence of heterogeneity factors in the participant set and maximize the training effect and accuracy of federated learning. The effectiveness of this method was verified by using the Dirichlet distribution to perform non-IID data partitioning on the power system attack dataset and the hard disk fault detection dataset.

A Co-Estimation Framework for State of Charge and Parameters of Lithium-Ion Battery With Robustness to Aging and Usage Conditions

Aging and usage conditions affect the battery parameters such as capacity and changes in the open-circuit voltage and internal resistance dependencies on the state of charge. This paper proposes an on-board strategy for the simultaneous estimation of these parameters and their robust evaluation during the battery life. The proposed co-estimation framework consists of a set of interconnected subsystems grounded on the integration of recursive least-squares techniques and a Luenberger-like observer which are independently designed by relying on moving averages of voltage and current measurements. Each subsystem is separately activated through logic variables which select the operating conditions proper for the estimation purposes and allows tracking of model parameters variations. The effectiveness of the solution is shown over experiments with a cylindrical LG M50T INR21700 Li-ion cell with NMC cathode and graphite/silicon anode.

Adaptive active fault-tolerant dynamic surface load following controller for a modular high-temperature gas-cooled reactor

The control of nuclear reactors is one of the most challenging requirements in performing the load following operation mode; however, most of the previous studies either do not consider the adverse effects of the lumped uncertainty or rely on the assumption of possessing healthy actuators without any faults. Besides, the recently published backstepping load following controllers may result in the problem of “explosion of complexity” due to repeatedly differentiating the virtual control laws. To this end, this paper provides an adaptive active fault-tolerant dynamic surface control strategy for the load following problem of a modular high-temperature gas-cooled reactor suffering from lumped uncertainty and actuator faults. The main contributions of this paper are threefold. (i) a radial basis function neural network is introduced to real-timely learn the lumped uncertainty that contains model uncertainties, parameter variations, external disturbances, and unmeasured states; (ii) a dynamic surface control technique is employed to cope with the problem of “explosion of complexity” induced by traditional backstepping approach; and (iii) an active fault-tolerant controller, incorporated with a fault detection observer and a fault estimator, is developed such that the reliability and safety of the modular high-temperature gas-cooled reactor system could be enhanced. With Lyapunov stability theory, both the stability of the closed-loop system and the error convergence are proved. At the end, comparative simulation studies and analysis using quantitative performance indices indicate the proposed control strategy’s superiority in dealing with lumped uncertainty and actuator faults.

Assessment of the variability of the I-V characteristic of HfO2-based resistive switching devices and its simulation using the quasi-static memdiode model

Variability of the conduction characteristics of filamentary-type resistive switching devices or resistive RAMs (RRAMs) is a hot research topic both in academia and industry because it is currently considered one of the major showstoppers for the successful development and application of this technology. In this work, we thoroughly investigate the statistics of the cycle-to-cycle (C2C) variability observed in the experimental current–voltage (I-V) curves of HfO2-based memristive structures using the fitdistrplus package for the R language. This exploratory analysis allows us to identify which parametric probability distributions are the most suitable candidates for describing our data. This study involves graphical tools such as the density, skewness-kurtosis (S-K), and quantile–quantile (Q-Q) plots. The analysis is completed with the aid of goodness-of-fit statistics (Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling) and criteria (Akaike’s and Bayesian). The selected distributions are incorporated into the SPICE script of the quasi-static memdiode model for resistive switching devices and used for simulating uncorrelated C2C variability. Finally, a one-way sensitivity analysis is carried out in order to test the impact of the model parameters variation in the output characteristics of the device.

Genetic-Algorithm-Guided Development of Parametric Aeroelastic Reduced-Order Models with State-Consistence Enforcement

Data-driven parametric reduced-order models (ROMs) in state-space form are valuable tools for rapid aeroelastic (AE) analysis and aerostructure control synthesis. However, the issue of state inconsistence (significant variations in model parameters over tradespaces) makes ROMs noninterpolatable, and therefore unable to accommodate use over broad flight parameter space. This paper presents a holistic framework that combines a system identification technique with state-consistence enforcement (SCE) and a genetic algorithm (GA) for the automated development of interpolatable AE ROMs across broad flight regimes. The SCE technique introduces a regularization term to the AutoRegressive model with eXogenous inputs (ARX) to specifically penalize model parameter variation between flight conditions. The GA autonomously guides the ROM development process toward optimal SCE-ARX hyperparameter selection that balances between model parameter variations and ROM accuracy. The GA-guided SCE-ARX approach is applied to build a parametric AE-ROM database at selected flight conditions, which, because of its state consistence, can be interpolated to create ROMs at any interstitial conditions, where training data or ROMs are not initially available, hence rapidly establishing the full coverage of the entire parameter space. The ROMs generated by the proposed method are compared with those by ARX, SCE, and GA-guided ARX in prediction accuracy. The individual and combined effects of SCE and GA on model parameter variation and ROM interpolatability are thoroughly investigated. The present method demonstrates the most accurate and robust performance for parametric ROM construction across the broad flight envelope.

Optimization workflow to parameterize elastomer material models based on arbitrary time‐domain measurement data

AbstractPredicting the behavior of elastomer components always requires experimental characterization of their nonlinear mechanical properties. This involves measuring the ratio of stresses and strains and the dissipated energy per load cycle in harmonic deformations of test specimens. Characteristic values, such as the elastomer's stiffness and damping, and their dependence on amplitude and frequency are then calculated and used to identify material model parameters. However, determining these characteristic values imposes significant requirements on test setup and control, as they are only meaningful when calculated from ideally sinusoidal and steady‐state load sequences. This can result in unnecessary methodological errors for any real‐world measurement. This contribution presents a method for identifying material model parameters that omits determining characteristic values, greatly reducing testing times and prediction errors. The method involves an optimization‐based variation of model parameters to directly match the stress response of a material model with measured stresses. Its independence from ideal test sequences is shown by applying the method to numerically generated measurement data with artificial control errors. Application to real measurement data reveals a possible reduction of testing times from about 2 h to below 35 s while preserving the model's accuracy.

Metabolic-fluid dynamics model construction and scale-down design for an industrial penicillin chrysogenum fermentation with combined dissolved oxygen and glucose concentration dynamics

This study focuses on the metabolic impacts of simultaneous glucose and oxygen concentration gradients on penicillin production in an industrial-scale fermentor, using the computational fluid dynamics-cellular reaction dynamics approach. Inclusion of oxygen-coupling considerably impacts the glucose uptake and resulting penicillin productivity. This is characterised by six metabolic regimes; lifeline data reconstructed from experimental results, recorded from the cellular perspective, indicates rapid dynamics in glucose and dissolved oxygen uptake by the microorganisms. The results are highly sensitive to variations in the oxygen-related model parameters, requiring accurate insight into the multiphase hydrodynamics and metabolic processes. Hypothetical scenarios with stronger glucose-oxygen limitations than tested experimentally were further explored. A precision scale-down (SD) simulator was designed based on the lifeline data, requiring considerable operational dynamics, with increasing system complexity and implementation difficulty. These insights may inspire further research into alternative SD configurations better suited to mimic the rapid dynamics of large-scale fermentation processes.

Cost-effectiveness of Follow-up CT for Incidental Ascending Aortic Dilatation

To evaluate the cost-effectiveness of CT follow-up strategies for incidental aortic dilatation. In this cost-effectiveness analysis, a simulation model was developed with 1 000 000 adult patients aged 55-75 years with incidentally detected dilated aortas measuring 40-50 mm. Follow-up CT strategies were evaluated for various patient age- and aortic size-based cutoffs. Follow-up frequency ranged from 1 to 3 years, as well as a single follow-up CT examination at 1 year. Patient survival was determined by risk of aortic dissection or rupture and surgical- and age-based mortality. Costs and quality-adjusted life-years (QALYs) were calculated for each strategy within the simulated cohort. A probabilistic sensitivity analysis was performed by varying model parameters. The cost-effective strategy with the highest QALYs under a willingness-to-pay threshold of $100 000 per QALY was follow-up CT for patients younger than 60 years with aortas measuring at least 40 mm in diameter every 3 years (incremental cost-effectiveness ratio, $62 511; 95% CI: $52 168, $77 739). With this strategy, follow-up imaging was needed for only 17% of dilated aortas in the cohort. Probabilistic sensitivity analysis demonstrated that the cost-effective strategies at $100 000 per QALY threshold included the following: no follow-up for patients with aortas smaller than 50 mm (39% of simulations), follow-up every 3 years for patients younger than 55 years with aortas measuring at least 45 mm (21%), and follow-up every 3 years for patients older than 65 years with aortas measuring at least 40 mm (14%). Follow-up CT for an incidentally detected dilated ascending aorta smaller than 50 mm is likely not cost-effective in patients older than 60-65 years.Keywords: CT, Thorax, Vascular, Aorta, Cost-Effectiveness, Cost-Benefit Analysis Supplemental material is available for this article. © RSNA, 2023See also commentary by Shen and Fleischmann in this issue.

Higher-order complex cubic quintic Ginzburg-Landau equation: Chirped solitary waves

Propagation characteristics of the chirped dissipative solitary waves are investigated within the framework of higher-order complex cubic quintic Ginzburg-Landau equation. A potentially rich set of exact chirped dissipative pulses, such as, bright, dark, grey, antidark, kink, antikink is derived in the presence of the self-steepening, self-frequency shift and nonlinear gain/loss. The linear stability results are corroborated by the direct numerical simulations. The effect of the variation of model parameters on physical quantities, like the speed, amplitude and chirping, is explored.

Incorporating Functional Response Time Effects into a Signal Detection Theory Model.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions.

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.