Sensitivity Of Model Parameters Research Articles

Optimization design and performance analysis of a rotary vane magnetorheological damper for vehicle suspension based on NSGA-II

Rotary magnetorheological dampers (RMRDs) have good application prospects in semi-active suspension due to their compact structure and good sealing performance. However, the existing RMRD cannot be practically applied in the field of high-torque demand. Therefore, this paper aims to improve the mechanical performances and response-ability of a high-output torque rotary vane magnetorheological damper (RVMRD) for vehicle suspension. Firstly, the mechanical, equivalent magnetic circuit and equivalent circuit models of the RVMRD are established, and the one-at-a-time (OAAT) method is used to analyze model parameter sensitivity. Then, the multi-objective optimization design of the RVMRD is based on the non-dominated sorting genetic algorithm (NSGA-II), and the magnetic field formation time of the initial and optimal RVMRD is analyzed by transient electromagnetic field analysis. Finally, to verify the validity of the multi-objective optimization design of RVMRD, the initial and optimal RVMRD were tested for mechanical performances and response time. The mechanical performance testing results show that the output torque and dynamic range of the optimal RVMRD are significantly improved, and maintain good mechanical performance at different temperatures. The response time testing results show that the optimal RVMRD has a phased improvement in response-ability under different step currents, which can effectively suppress vibration and shock in high-frequency dynamic environments The research results in this paper provide helpful guidance for developing and optimizing an RMRD, which has excellent potential for application in the large-scale mechanical industry with space-limited and high-torque requirements.

Controls on coastal saline groundwater across North America

Abstract Groundwater is crucial to sustaining coastal freshwater needs. About 32 million people in the coastal USA rely on groundwater as their primary water source. With rapidly growing coastal communities and increasing demands for fresh groundwater, understanding controls of continental-scale coastal groundwater salinity is critical. To investigate what hydrogeological factors (e.g., topography, hydraulic conductivity) control coastal saline groundwater at continental scales, we have simulated variable-density groundwater flow across North America with the newly developed Global Gradient-based Groundwater Model with variable Densities (G3M-D). The simulation results suggest that under a steady climate and pre-development conditions (i.e., steady 30-year mean groundwater recharge, no withdrawals nor sea level rise) saline groundwater is present in 18.6% of North America's coastal zone, defined as up to 100 km inland and up to 100 m above mean sea level. We find that the coastal zone is particularly vulnerable to containing saline groundwater at low hydraulic gradients (<10-4) and large hydraulic conductivities (>10-2 m day-1). To analyze model parameter sensitivities, i.e., which parameters control the resulting distribution of saline groundwater, we utilize the inherent spatial model variability. We find that hydraulic gradient, topographic gradient, hydraulic conductivity, and aquifer depth are important controls in different places. However, no factor controls coastal groundwater salinization alone, suggesting that parameter interactions are important. Using G³M-D based on G3M, a model that previous work found to be strongly controlled by topography, we find no controlling influence of recharge variability on the saline groundwater distribution in North America. Despite a likely overestimation of saline interface movement, the model required 492 000 years to reach a near-steady state, indicating that the saline groundwater distribution in North America has likely been evolving since before the end of the last ice age, approximately 20 000 years ago.

Impact of media coverage on the transmission dynamics of TB with vaccines and treatment.

Tuberculosis (TB) is one of the deadly infectious diseases affecting millions of individuals throughout the world. The main objective of this study is to investigate the impact of media coverage on the transmission dynamics of TB with vaccine and treatment strategy using mathematical model analysis. In the qualitative analysis of the proposed model we proved the existence, uniqueness, positivity, and boundedness of the model solutions, investigated both the disease-free and endemic equilibrium points, computed the basic and effective reproduction numbers using next generation matrix approach, analyzed the stability analysis of the equilibrium points, the backward bifurcation using the Castillo-Chavez and Song theorem and we re-formulated the corresponding optimal control problem and analyzed by applying the Pontryagin's Minimum Principle. In the model quantitative (numerical) analysis part, we performed the model parameters sensitivity analysis and carried out numerical simulation to verify the qualitative analysis results. The findings of the study indicate that if the reproduction number is less than one, the solution converges to the disease-free state, signifying the asymptotic stability of the TB-free steady state. Moreover, the existence of a backward bifurcation shows that the disease-free equilibrium coexists with one or more endemic equilibria, even when the basic reproduction number is less than 1. Furthermore, it is found that as media efficacy increases, the disease infection rate decreases, which consequently leads to an increase in prevention and treatment control strategies.

Random files for fission fragment evaporation in TALYS: a total Monte Carlo approach

In this study, we have applied the Total Monte Carlo (TMC) methodology in the simulation of the de-excitation process of primary fission fragments (FF) within the TALYS code. Our objective was to develop a method for assessing fission model deficiencies and parameter sensitivities. The input fission fragment data used by TALYS were systematically varied. This was done using the GEF code to generate 10,000 random files by randomizing the 94 model parameters of GEF that influence both fission yields and their energy distributions. The GEF parameters were varied randomly within 3% of the default value, assuming a normal probability distribution. As a result of this parameter variation, significant changes could be identified for several observables, including the multiplicities of γ rays and prompt neutrons, as well as their spectra. Additionally, we investigate the impact of angular momentum distribution on the de-excitation data of both GEF and TALYS. Finally, we present an attempt to construct a best parameter file benchmarked against evaluated nuclear data files.

An innovative coal permeability model based on elastoplastic mechanics: Development and verification

With the continuous mining of shallow coal resources, deep mining has increasingly become the norm. However, the migration mechanism of coalbed methane (CBM) in coal seams becomes exceptionally complex due to the combined influence of multiple factors in deep mining, posing considerable challenges to coal and gas co-mining. Therefore, studying the coal's mechanical behavior and seepage evolution mechanisms during deep mining is necessary. This study established a coal permeability model based on elastoplastic mechanics, considering the impacts of coal matrix destruction on the average fracture aperture. It assumed that the fracture aperture follows an exponential distribution and further introduced plastic strain to characterize the damage process in coal. The proposed permeability model was validated using the indoor experimental data. Subsequently, the control mechanisms of force-heat coordination effects on coal permeability were discussed, and the sensitivity of model parameters was analyzed. The results demonstrated that the established permeability model effectively described the evolution of coal permeability under the combined impacts of temperature and effective stress. Moreover, the fracture number ratio (η) and the influence coefficient of plastic strain increment on the average fracture aperture (β) not only connected the dilation of microfractures and plastic deformation in coal but also effectively reflected the relationship between permeability and plastic deformation during the failure process of coal. The results presented in this paper contributed to understanding the evolution of permeability during coal and gas co-mining, which should be of great significance for reducing coal and gas outburst hazards.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

Stability and computational analysis of Influenza-A epidemic model through double time delay

Delay factors demonstration has a significant role in controlling a strain of infectious disease instead of a pharmaceutical strategy. According to the World Health Organization (WHO), 3–5 million cases are reported annually and approximately 290,000 to 650,000 respiratory deaths annually. So, in the present study, we develop a delayed mathematical model based on delay differential equations (DDEs) for the influenza epidemic using a deterministic approach by introducing double delay parameters. The four distinct sub-populations are considered susceptible, exposed, infected, and recovered. For the rigorous analysis, the fundamental properties of the model like positivity, boundedness, existence, and uniqueness, were studied. The influenza-free equilibrium (IFE) and influenza-existing equilibrium (IEE), are the two nonnegative equilibrium points that the model demonstrates. Both locally and globally, the asymptotic stability of the equilibrium points of the model is established and shown under specific situations of reproduction number. Additionally, investigated the model's parameter sensitivity and determined the relative sensitivity of each parameter. Both standard and nonstandard methods—such as Euler, Runge-Kutta, and nonstandard finite difference with a delayed sense—are presented to make computational analysis support a dynamical analysis and the best visualization of results. The stability of the non-standard finite difference scheme is thoroughly analyzed around the steady states of the model. Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

A novel parabolic model driven by double phase flux operator with variable exponents: Application to image decomposition and denoising

This work tackles a novel parabolic equation driven by a nonlinear operator with double variable exponents, aiming to decompose and denoise images. Our primary approach involves enhancing classical models based on variable exponent operators by considering a novel nonlinear operator having a double-phase flux with unbalanced growth. We begin initially by analyzing the theoretical solvability of our model. Employing the Musielak-Orlicz space, we establish a suitable functional framework for investigating the proposed model. Subsequently, we use the Faedo-Galerkin approach to establish the existence and uniqueness of a weak solution for our problem. Furthermore, we provide a demonstration showcasing how our model preserves solution positivity, emphasizing this as a consistent feature of the proposed model. To evaluate our theoretical results, we present various numerical simulations on some grayscale and medical images (Magnetic Resonance Images (MRI)). These simulations covered aspects like decomposition, robustness and behavior sensitivity of model parameters with visual and quantitative comparisons. The obtained numerical results strongly support the efficiency of the proposed model in preserving features, reducing artifacts and deleting noise in comparison to some well-known existing state-of-the-art models. Furthermore, the proposed model quantitatively surpasses the competitive models in terms of several well-known criteria, namely the Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index (SSIM) and the Mean-Square Error (MSE).

Performance evaluation and multi-objective optimization of a tubular indirect evaporative cooler integrated with moisture-conducting fibers

Indirect evaporative cooling (IEC) technology is an energy-efficient approach for regulating the indoor thermal environment of buildings. The conventional tubular indirect evaporative cooler (TIEC) may have a relatively low cooling efficiency due to poor wettability issues. The application of moisture-conducting fibers provides a feasible way to solve the above problem. However, the integration of moisture-conducting fibers with TIEC is still in the exploratory stage. This study proposed a novel moisture-conducting fiber-assisted TIEC and conducted a multi-objective optimization. An experimental facility and theoretical model of the proposed moisture-conducting fiber-assisted TIEC were developed. Based on the numerical model validated by experiments and response surface methodology (RSM), the regression models for performance prediction of the cooler were established. Eight input parameters including inlet air parameters, operating parameters and geometric parameters were selected, and four performance evaluation indicators were chosen as output responses. The parameter sensitivity of the regression models was analyzed. The multi-objective optimization was performed by considering the influence of different relative weights assigned to the output responses. Furthermore, the performance of the optimized cooler applied in different climate zones was predicted. The results showed that the product air temperature drop could achieve 8.8–11.3 °C after cooling by the cooler. The established regression models can predict the performance of the moisture-conducting fiber-assisted TIEC conveniently and effectively, which is expected to guide the design and optimization of engineering practices.

Parameter sensitivity of a wood chips flow model

AbstractA computational fluid dynamics (CFD) study of the parameter sensitivity of a wood chips model was performed on an industrial impregnation vessel, which is the first step in a continuous cooking system. The solid and liquid phases were both treated as continua and it was found that the continuum model for the solid wood chips phase could capture the previously observed oscillating formation of arches in the contracting part of the vessel, which will occur at different levels of volume fraction depending on the material constants. The parameters that were examined are the solid pressure, permeability, viscosity, and wall friction. It was found that all the parameters strongly affect the distribution of the wood chips in the vessel as well as the oscillation effects, hence also the flow field which is important to accurately predict in order to ensure optimal performance of the impregnation vessel. Thus, correct material data for these types of simulations are crucial to the outcome and should be chosen for the appropriate situation and bio‐material.

Impacts of Land Use on Runoff and Sediment Dynamics in Tropical Watersheds: A Case Study in Bogowonto Upper Watershed

Land use changes in tropical regions have increased, leading to rising environmental stress in Java, Indonesia. Food shortages have driven land conversion and expansion, which increases peak flows during the rainy season and reduces water storage in the dry season, heightening flood risks. Research on integrated catchment hydrology is crucial. This study examines the relationship between land use, runoff, and sediment in the Bogowonto Upper Watershed using SWAT hydrological modeling. The SWAT model helps understand hydrological processes at the watershed scale and the impact of land use changes on runoff and sediment dynamics. The sensitivity of SWAT model parameters varies in the Bogowonto Upper Watershed. Runoff sensitivity analysis indicates a +62% increase with a 50% change in CN value, showing high sensitivity. A 50% change in vegetation cover results in a +50% model output, indicating moderate sensitivity. Slope, Ksat (saturated hydraulic conductivity), and bulk density are fairly sensitive, while AWC is slightly sensitive. For sediment, a 50% increase in CN value results in a +47% change, and a 50% increase in vegetation cover leads to a +58% model output, showing moderate sensitivity. The model, run from 2014-2019, shows excellent accuracy with NSE of 0.82, RRMSE of 0.43, R² of 0.83, and PBIAS of 9.8%.

Deformation Analysis of Existing Buildings Affected by Shield Tunnels Based on Intelligent Inversion and Measured Data

In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlement and may cause potential safety hazards to the surrounding existing buildings, directly affecting the safety of urban operation. When simulating and predicting surface settlements, the small-strain soil hardening model can more accurately characterize the mechanical parameters of soil. Nevertheless, its parameters are numerous and complicated to determine accurately, so parameter inversion is needed to determine the accurate parameters of the soft soil layer in order to more accurately predict the surface settlement. This study uses the EFAST method to analyse the sensitivity of the HSS model parameters of soft soil strata. It is determined that the parameters that have the most significant impact on the surface settlement are the reference tangent modulus, rebound modulus, and effective cohesion. Then, XGBoost’s fast calculation speed and high precision of SSA inversion are used to inverse and optimize the parameters with high sensitivity. Finally, according to the parameters of the soft soil layer obtained from inversion and measured data, the settlement deformation and safety behaviour of existing buildings are analysed. Combined with the actual shield tunnel project in a city along a river, the inversion calculation shows that the overall average error of the transverse monitoring section is 1.04 mm, and the average maximum error of each monitoring point in the overall shield process is 2.87 mm. The prediction effect is significantly improved compared with the original parameters. The accuracy of the inversion of soil layer parameters is verified from the perspective of time and space. The average settlement of the river embankment foundation is 2.5 mm. Compared with the original parameter data, the prediction results have been greatly improved, and the settlement deformation results are more consistent with the measured data.

Post-COVID inflation and the monetary policy dilemma: an agent-based scenario analysis

The economic shocks that followed the COVID-19 pandemic have brought to light the difficulty, both for academics and policy makers, of describing and predicting the dynamics of inflation. This paper offers an alternative modelling approach. We study the 2020–2023 period within the well-studied Mark-0 Agent-Based Model, in which economic agents act and react according to plausible behavioural rules. We include a mechanism through which trust of economic agents in the Central Bank can de-anchor. We investigate the influence of regulatory policies on inflationary dynamics resulting from three exogenous shocks, calibrated on those that followed the COVID-19 pandemic: a production/consumption shock due to COVID-related lockdowns, a supply chain shock, and an energy price shock exacerbated by the Russian invasion of Ukraine. By exploring the impact of these shocks under different assumptions about monetary policy efficacy and transmission channels, we review various explanations for the resurgence of inflation in the USA, including demand-pull, cost-push, and profit-driven factors. Our main results are fourfold: (i) without appropriate fiscal policy, the shocked economy can take years to recover, or even tip over into a deep recession; (ii) the success of monetary policy in curbing inflation is primarily due to expectation anchoring, rather than to the direct economic impact of interest rate hikes; (iii) however, perhaps paradoxically, strong inflation anchoring is detrimental to consumption and unemployment, leading to a narrow window of “optimal” policy responses due to the trade-off between inflation and unemployment; (iv) the two most sensitive model parameters are those describing wage and price indexation. The results of our study have implications for Central Bank decision-making, and offer an easy-to-use tool that may help anticipate the consequences of different monetary and fiscal policies.

Urban energy transition in renewable energy management considering policy and law challenges and opportunities in the pursuit of clean and sustainable urban environments

This research presents a novel approach to enhance sustainable energy development based on wind turbine (WT) output power prediction through the development of a hybrid deep learning model, considering policy and law challenges associated with the problem. The proposed model integrates a Self-Supervised Generative Adversarial Network (GAN) with Attention-based Recurrent Neural Networks (RNNs). The Self-Supervised GAN contributes to improved feature representation learning, while Attention-based RNNs capture temporal dependencies for accurate WT output power forecasting considering political challenges. Additionally, a Modified Adaptive Flower Pollination Algorithm (AFPA) is introduced to optimize the selection of the GAN model structure in view of the legal and political objectives. This adaptive approach refines the architecture of the GAN, enhancing its ability to generate realistic and informative features for the subsequent Attention-based RNNs. The efficacy of the hybrid model is demonstrated through real-world applications in an urban area, emphasizing sensitivity and stability analyses. A thorough investigation into the sensitivity of key model parameters, including the number of hidden units in the Attention-based RNNs and GAN structures, is conducted to fine-tune the model's performance. Stability analysis is employed to assess the robustness of the proposed method under dynamic conditions, ensuring its reliability in practical scenarios. Results indicate that the hybrid model, guided by the Modified AFPA, outperforms existing approaches in WT output power prediction. The proposed methodology showcases the significance of combining state-of-the-art deep learning techniques with adaptive optimization algorithms for accurate and stable predictions in urban energy management applications.

New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-pT charged hadrons, D mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient q̂. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses. Published by the American Physical Society 2024

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

Computational Modeling of Sodium Fluorescein Disposition in Ex Vivo, Machine-Perfused Livers with Ischemia-Reperfusion Injury

Rationale: Quantitation of bile formation machinery activity through biliary sodium fluorescein (SF) clearance is useful for assessing liver viability. Studies have shown that the bile-to-plasma SF ratio is inversely correlated with warm ischemia time (WIT) in rats, which is associated with ischemia-reperfusion injury (IRI)-induced intracellular internalization of the multidrug resistance protein 2 (MRP2) transporter (doi: 10.1152/ajpgi.00038.2022). Computational modeling studies by Monti et al. have demonstrated the sensitivity of biliary SF clearance to MRP2 transporter activity in vivo (doi: 10.48550/arXiv.2302.05511). In vivo experimentation is pertinent for establishing physiological variations occurring during IRI, but transplantation often requires a period of ex vivo machine perfusion (MP). The effects of total hepatectomy, WIT, cold ischemia time (CIT), and MP on biliary function remain unclear. We aim to develop a model for quantitative interpretation of biliary SF clearance kinetics in MP livers to estimate parameters descriptive of the dominant processes determining hepatic SF uptake kinetics in MP livers subjected to different periods of WIT. We hypothesize that increasing WIT will lead to a decrease in the maximal transport velocity (Vmax) parameter for MRP2, which is reflective of decreased biliary SF clearance and diminished liver viability. Methods: Using established protocols (doi: 10.3389/frtra.2023.1215182), livers (n = 3-5) were obtained from anesthetized, male, Sprague-Dawley rats and subjected to 0-, 10-, 20-, or 30-min WIT through ligation of the portal vein and hepatic artery. Livers were removed and subjected to 3 hours of CIT on ice. Livers were attached to a custom normothermic MP system, 0.4 mg/kg SF were added to the MP system’s perfusate-containing reservoir, and SF fluorescence measurements in perfusate and bile were obtained and converted to concentration measurements using standard curves. We modified the liver-centric model described in Monti et al. to account for MP by replacing the liver input functions with ordinary differential equations for SF and its conjugate SF glucuronide (SFG) kinetics in the MP system’s reservoir. The model also accounts for three liver regions (sinusoid, hepatocytes, bile) and major processes, including hepatic transporter kinetics and SF metabolism into SFG, which govern SF dynamics in different model regions. We fit the model solutions using a pseudo-Monte Carlo parameter estimation strategy to each of the datasets by allowing the Vmax parameters to vary, while others were fixed to physiologic values. Results: Time course data for IRI conditions showed a modest decrease in biliary SF with 10- and 20-min WIT and a marked change in biliary SF kinetics with 30-min WIT when compared to control. The MP model was fit to each dataset individually and the model was able to provide a good fit to each dataset. Comparison of Vmax parameters estimated from fitting showed a decrease in MRP2’s Vmax with increasing WIT relative to control. Sensitivity analysis supported our finding by demonstrating that MRP2’s Vmax­ was the most sensitive model parameter. Conclusions: We modified an existing in vivo biliary SF clearance model to account for MP livers and fit the new model to biliary SF clearance data for ex vivo MP livers with varying amounts of WIT. We found that the new model was able to fit biliary SF clearance data for ex vivo MP livers and that the Vmax for MRP2 decreased during MP with increasing WIT. Funded in part by 1F30AI179084-01 to C.M. and NSF DMS 2153387 to R.D. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

A mathematical model for mapping the insecticide resistance trend in the Anopheles gambiae mosquito population under climate variability in Africa

The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. μPRCC = 0.85958, p-value < 0.001). In this study, the integration of high-resolution covariates with the SIR model had a significant impact on the spatial and temporal variation of IR among mosquito populations across Africa. Importantly, we demonstrated a clear association between climatic variability and increased IR (width = [0–3.78], α = 0.05). Regions with high IR variability, such as western Africa, also had high malaria incidences thereby corroborating the World Health Organization Malaria Report 2021. More importantly, this study seeks to bolster global malaria combat strategies by highlighting potential IR ‘hotspots’ for targeted intervention by National malria control programmes.