Values Of Model Parameters Research Articles

Transit cosmological models in F(R,T¯)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F(R,{\\bar{T}})$$\\end{document} gravity theory

In the present paper, we investigate some exact cosmological models in F(R,T¯)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F(R,{\\bar{T}})$$\\end{document} gravity theory. We have considered the arbitrary function F(R,T¯)=R+λT¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F(R, {\\bar{T}})=R+\\lambda {\\bar{T}}$$\\end{document} where λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda $$\\end{document} is an arbitrary constant, R,T¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R, {\\bar{T}}$$\\end{document} are respectively, the Ricci-scalar curvature and the torsion. We have solved the field equations in a flat FLRW spacetime manifold for Hubble parameter and using the MCMC analysis, we have estimated the best fit values of model parameters with 1-σ,2-σ,3-σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1-\\sigma , 2-\\sigma , 3-\\sigma $$\\end{document} regions, for two observational datasets like H(z) and Pantheon SNe Ia datasets. Using these best fit values of model parameters, we have done the result analysis and discussion of the model. We have found a transit phase decelerating-accelerating universe model with transition redshifts zt=0.4438-0.0790+0.1008,0.3651-0.0904+0.1644\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{t}=0.4438_{-0.0790}^{+0.1008}, 0.3651_{-0.0904}^{+0.1644}$$\\end{document}. The effective dark energy equation of state varies as -1≤ωde≤-0.5176\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-1\\le \\omega _{de}\\le -0.5176$$\\end{document} and the present age of the universe is found as t0=13.8486-0.0640+0.1005,12.0135-0.2743+0.6206\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t_{0}=13.8486_{-0.0640}^{+0.1005}, 12.0135_{-0.2743}^{+0.6206}$$\\end{document} Gyrs, respectively for two datasets.

Modeling of Wind Power Plants and Their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. By changing the structure of the model, projections on the future of wind power plant generation can be estimated. Several scenarios being developed include adding turbines in the Sidrap and Jeneponto regencies to increase the fulfillment ratio of wind energy and scenarios to reduce CO2 emissions using solid direct air capture (S-DAC). The data and information used in this research come from the Central Statistics Agency, articles on wind energy power plants, and data from related previous studies. These models and scenarios can be applied in other regions by adjusting the parameter values of the case study model. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. With the addition of 25 turbines in Sidrap and 20 turbines in Jeneponto, the average fulfillment ratio is estimated to increase by around 7% due to increased production. Meanwhile, total CO2 emissions are estimated to decrease by approximately 45% due to solid direct air capture.

Dark energy nature of logarithmic f(R,Lm)-gravity models with observational constraints

This work explores the dark energy nature of logarithmic [Formula: see text]-gravity models with observational constraints, where [Formula: see text] represents the matter Lagrangian for perfect fluid and R is the Ricci-scalar curvature. With a matter Lagrangian [Formula: see text], a flat FLRW space–time metric and an arbitrary function [Formula: see text], where [Formula: see text] is a positive model parameter, we have derived the field equations of this model. By using the Hubble function, we have been able to solve the energy conservation equation and create a relationship between [Formula: see text], [Formula: see text] and [Formula: see text]. Using the most recent two observational datasets as 32 [Formula: see text] and 1048 Pantheon SNe Ia datasets, we conducted MCMC analysis. We have obtained best fit values of model parameters with [Formula: see text] errors and using these values, we have explored the cosmological properties of the model. We have performed om diagnostic analysis for the model and estimated the present age of the universe. Thus our derived model presents a transit phase decelerating to accelerating model without dark energy term [Formula: see text]. We found that the effective equation of state parameter is in the range [Formula: see text] over [Formula: see text] with present value [Formula: see text].

Spatio-temporal characterization of earthquake sequence parameters and forecasting of strong aftershocks in Xinjiang based on the ETAS model.

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately.

Probing the universe's expansion dynamics: The linear correction scenario perspective on dark energy

In this study, we investigate the cosmological evolution of the universe, focusing on the KMMS models in a model-independent approach, particularly the scenario involving linear correction, given by E2(z)=A(z)+β(1+γB(z)), where A(z)=Ωm0(1+z)3 and B(z)=z. By analyzing recent Cosmic Chronometers (CC) and the Pantheon+ samples, we determine the best-fit values of model parameters using a Markov chain Monte Carlo analysis. Our models exhibit unique dynamics, transitioning from super-exponential to exponential expansion, with a significant shift at redshift zt=0.83−0.10+0.11 and present deceleration parameter q0=−0.69−0.17+0.17. The jerk parameter exceeds 1, indicating a faster change in acceleration than predicted by ΛCDM. The energy density parameter is consistent with Planck observations, and the dark sector evolution follows the expected thermal history. The EoS parameter approaches ωde=−1 over time, with a current value of ωde0=−1.06±0.12, aligning with the phantom phase. Our model presents a new dark energy alternative, emphasizing the importance of considering models like KMMS in understanding cosmic evolution.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

A numerical model simulating cyclic behavior of high-strength steel

High-strength steel (HSS) can effectively and economically design structural members that withstand large forces induced by extreme events such as earthquakes. To evaluate the seismic performance of HSS members and structures using nonlinear finite element (FE) analyses, using an accurate material model is important, which can simulate the inelastic cyclic behavior of the HSS. The peculiar material behavior of HSS needs to be considered in the model. This study used a combined hardening model to construct the material model. The configuration of the model and the constituent model parameter values are determined to precisely simulate the low-cycle fatigue (LCF) behavior of HSS. An efficient particle swarm optimization (PSO) algorithm is used to determine parameter values with LCF test data of 54 individual HSS coupons. In additions, to conveniently determine the model parameter values with only monotonic tensile test data instead of conducting expensive LCT tests, empirical equations are proposed. It is shown that the LCF curves of HSS can be accurately simulated using the constructed material model with the proposed equations.

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Abstract. Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.

A distributed constant equivalent circuit model and a method for estimating parameters for paper-based lactate biofuel cells

In this study, a distributed constant-type equivalent circuit model for paper-based lactate biofuel cells (PBFCs) where the anode and cathode are formed on the same plane was constructed, and a method of determining parameter values in the equivalent circuit model was proposed. When PBFC were prepared and the electrochemical impedance of the entire cell was measured, the Nyquist diagram produced a spectrum due to the current line distribution. Parameters, such as charge transfer and solution resistances, were calculated from the obtained spectrum using theoretical impedance equations derived from the proposed equivalent circuit model proposed. In addition, the behavior of the current distribution in the battery was evaluated in detail by moving the reference electrode on and performing local electrochemical impedance spectroscopy at multiple points on the electrode. The models and methods proposed in this study are good for evaluating systems affected by distributed current lines, such as flat batteries, and for determining cell design, electrode areas, and arrangements.

Inverse optimization in semi-definite programs to impute unknown constraint matrices

In a typical (forward) optimization problem, a decision-maker uses given values of model parameters to compute the values of decision variables. The goal in inverse optimization (IO) is instead to infer parameters that render given values of decision variables optimal. Most papers on IO utilize duality to impute objective function parameters. A corresponding literature for imputing constraint parameters is essentially non-existent, even for linear programs. The difficulty is that these IO problems include nonconvex bilinear constraints and/or objectives. We study inverse semi-definite programs (SDPs) where matrices on the left-hand-sides of constraints are unknown. These unknown matrices must satisfy some side constraints. We consider two types of such problems, depending on which SDP decision variable values are given: primal or dual. In each situation, we study three sub-cases based on whether both the right-hand-side coefficients and the objective function matrix are known or only one of these two is known. This creates six variants of the inverse problem. For each variant, we first look for sufficient conditions, either on the side constraints or on problem structure, under which the nonconvex bilinear inverse problem can be simplified substantially. In particular, it either has a trivial solution or can be reformulated as a (set of) convex program(s). When such conditions are not evident, we propose three tailored heuristics called the Convex–Concave Procedure, Sequential Semi-definite Programming, and Alternate Convex Search. We perform computational experiments to gain insights into the performance of these three methods.

Calibration and validation of DEM parameters of food waste with high total solid content (≥20%) using physical experiments and EDEM simulations

Since discrete element method (DEM) parameters are important for the calculation accuracy of EDEM simulations, the DEM parameters of food waste with high total solid content (≥20%) are calibrated and validated by experiments and simulations in this paper. The physical properties of food waste are obtained by physical experiments, and then the optimal values of DEM model parameters recommended for food waste can be obtained from the GEMM database. Finally, the repose angle and the average torque obtained by EDEM simulations are compared with the experimental results to validate the calibrated DEM parameters of food waste. The average relative error of the repose angle between the experimental values and the simulation values is 3.23%, and the average relative error of the average torque value is 5.29%. That means the DEM parameters of food waste calibrated in this paper are correct and can be applied for simulating the mixing performance of food waste with high total solid content (≥20%).

Statistical correlation between HSS model parameters and the method of parameter valuation

Small-strain hardened soil model is suitable for fine numerical simulation because it considers the compression and shear hardening of soil. However, setting the parameters of the model is difficult. Based on the analysis of the physical meaning of each parameter and the statistical analysis of parameter values of the small strain model reported by researchers, the empirical relationship between several parameters is obtained in this study. Accordingly, valuation methods of parameters are discussed.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries

The precise identification of electrical model parameters of Li-Ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal model parameter values. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-Ion batteries with different levels of complexity are used to fit the electrochemical impedance spectroscopy (EIS) data operating under different states of charge (SoC) and different operating temperatures. It is found that there is a tradeoff between ECM complexity, identification accuracy, and precision.

Analysis of time‐dependent mechanical behavior of polyethylene

AbstractNew data analysis based on a spring‐dashpot model is developed to provide very close simulation of stress variation in polyethylene (PE) when subjected to a multi‐relaxation (MR) test. The model consists of a quasi‐static branch and two (long‐ and short‐term) viscous branches. The study shows that viscous stresses applied to the two dashpots and the model parameters can be quantified as functions of displacement (stroke) by simulating closely the stress variation in relaxation. Using these parameters, influences of stress triaxiality and material grade on PE's stress response at relaxation stages are examined, and along with experimental data at the loading stages, spring constants in the two viscous branches are determined. The results indicate that among three PE grades, spring constants and their trend of variation are similar in the long‐term viscous branch, but not in the short‐term counterpart. The results also show that by decreasing specimen gauge length 10 times to increase stress triaxiality, spring constants in the two viscous branches become closer to each other. The work concludes that the new data analysis can determine all parameters in the model, which can then be considered to quantify the role of the viscous stress response for PE's load‐carrying performance.Highlights Use a simple model to simulate complex, multiple relaxation behaviors of PE. Determine all model parameter values to characterize PE's relaxation behavior. Illustrate the effect of stress triaxiality on PE's viscous stress response. Show the similarity and difference of the viscous stress response among 3 PEs.

Improving modeling of submerged canopy flows with a vortex-based Spalart–Allmaras model

Accurately modeling the hydrodynamics of submerged canopies is crucial for predicting sediment dynamics and geomorphodynamics. This paper introduces vortex-based Spalart–Allmaras (VBSA) models, considering the spatial structure of canopy-scale vortices and stem wakes. The VBSA models are innovative in incorporating the physics of canopy-overflow interaction. They were validated using experimental data across a wide range of velocities and canopy densities, and their performance was compared with other turbulence models (i.e., previous SA models and k-ε model). Despite lacking high-order numerical schemes, the VBSA models outperform other turbulence models, exhibiting less numerical dissipation and better replication of mean velocity and Reynolds shear stress profiles in the vortex penetration space and below the surface. A sensitivity analysis was conducted to identify optimal values for new model parameters. Analyses suggest that the porous canopy layer suppresses the mixing efficiency by reducing the turbulence length scale. The analysis of eddy viscosity flux balance reveals that the eddy viscosity is transferred to the canopy layer from the overflow by the canopy-scale vortices and is destructed in the canopy, with production being less important, particularly in large submergence scenarios.

The relationship between the inflation rate and the unemployment rate in Poland and their long-term associations with selected macroeconomic variables

The purpose of this paper is to examine the relationship between the inflation and unemployment rates in Poland and to analyze their long-term relationships with selected macroeconomic variables: the WIG20 index, consisting of the 20 largest Polish companies noted on Warsaw Stock Exchange, the USD/PLN exchange rate, the Brent crude oil index, and the interest rate on 10-year government bond yields. The main objective of the study is to determine the relationship between inflation and unemployment. In this study, a vector error correction model (VECM) was used to study long-run dependence. The impulse response function and forecast error variance decomposition were also used to examine the interactions between variables. There is one long-run relationship between the factors studied. Both the values of the VECM model parameters and the results of the impulse response function indicate that there is a negative relationship between inflation and unemployment in the short term. In the long term, there is a positive relationship, resulting in the stagflation phenomenon.

Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.

Comparative analysis of the application of direct search algorithms for estimating values of parameters in ecosystems models for the Neva Bay and Vistula Lagoon of the Baltic Sea

Comparative analysis of the application of direct search algorithms for estimating values of parameters in ecosystems models for the Neva Bay and Vistula Lagoon of the Baltic Sea

Abstract 2391: Towards better prediction of treatment resistance: Directly modeling progression from multiple lesions

Abstract Late-stage cancers in the clinic present as a set of dispersed tumors, each with distinct growth kinetics. As most patients enrolled in trials of experimental anticancer therapeutics have late-stage malignancies, it is important to understand the impact of data analysis methodologies on the ability to detect treatment effect. In particular, we are interested in the potential risk posed by aggregating individual tumor measurements from multiple-lesion clinical data into a single tumor burden measurement, as this may cause a loss of information. In this work, we use quantitative modeling to assess how analyzing multiple lesions separately versus using an aggregated readout affects tumor response and detection of progression free survival and relapse. To assess the impact of data aggregation, we built a two-population clonal dynamics model consisting of sensitive and resistant tumor cells undergoing growth in the presence or absence of treatment. We performed parameter sweeps to assess the impact of individual model parameters on progression free survival and relapse. We then extended these sweeps to the multi-lesion situation, using nonlinear regression to compare parameter values of aggregated models and the parameter values of the individual models that comprised it. From our simulations, the most influential parameters in determining rebound are the growth rate of the resistant cells and the fraction of these cells, while growth rate of sensitive cells and growth rate inhibition affect effectiveness of treatment. In analyzing the multiple lesion model in different situations, we found that estimates for progression free survival were significantly more sensitive when the lesions were analyzed separately, rather than being aggregated. Our work suggests that aggregating the lesions into a single readout loses impact of individual lesion growth rate inhibitions, impairing early detection as the ability to detect fast growing, small, resistant lesions (despite overall tumor burden decreasing) is lost. Taken together, our findings suggest that data aggregation causes a loss of information. Relapse can be predicted at earlier times when using the multiple lesion model compared to an aggregated model. Citation Format: Samuel Protich, Madison Stoddard, Lin Yuan, Douglas White, Dean Bottino, Arijit Chakravarty. Towards better prediction of treatment resistance: Directly modeling progression from multiple lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2391.