Parametric Modeling Approach Research Articles

Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in older participants.

With age, sensory, cognitive, and motor abilities decline, and the risk for neurodegenerative disorders increases. These impairments influence the quality of life and increase the need for care, thus putting a high burden on society, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, particularly ones that are potentially modifiable through lifestyle choices. However, large-scale studies investigating the influence of multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. We propose a machine learning model that simultaneously predicts multiple cognitive and motor outcome measurements on a personalized level recorded from one learned composite score. This personalized composite score is derived from a large set of multi-modal components from the TREND cohort, including genetic, biofluid, clinical, demographic, and lifestyle factors. We found that a model based on a single composite score was able to predict cognitive and motor abilities almost as well as a classical flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. The model identified several risk and protective factors for healthy aging and recovered physical exercise as a major, modifiable, protective factor. We conclude that our low parametric modeling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. We suggest validating this modeling approach in other cohorts.

A graph theory-based modelling method for multi-splitting-merging-channel refrigerant-cooled evaporator in battery thermal management

A multi-splitting-merging-channel refrigerant-cooled evaporator can provide non-uniform cooling capacity distribution to match a non-uniform distributed heat flux produced by a battery by properly arranged hierarchical refrigerant channel network, however the design of the refrigerant channels is extremely complex. The purpose of this study is to present a model to support the design of a multi-splitting-merging-channel refrigerant-cooled evaporator, and the model should have the functions of describing various channel network, predicting the refrigerant flow distribution in splitting and merging channels, and accurately presenting temperature distribution at unevenly distributed heat flux. In the model, the distributed parameter modelling approach is applied for predicting the thermal performance distribution; arbitrary kinds of control volumes are normalized into five standard types according to channel structures; the refrigerant flows with multiple splitting and merging are described by the calculation sequences of control volumes, which are generated based on the graph theory. Model validations are done on a small sized and a large sized evaporator samples by comparing the simulated results with the experiment data. It is shown show that, the average temperature deviations of the small sized and the large sized evaporator samples are 1.0 °C and 0.9 °C, respectively while their average pressure drop deviations are 2.8% and 2.1%, respectively. The validation result indicates that the accuracy of the proposed model is acceptable for engineering application.

A parametric modeling method for 3D woven composites considering realistic meso-structural characteristics

The precision of mesoscale simulation for three-dimensional woven composites (3DWCs) is intricately linked to the fidelity of the geometric representation. This paper aims to present a novel parametric modeling approach for generating representative volume element (RVE) of the 3DWCs while considering its realistic meso-structural characteristics. The real architecture of 3DWCs is defined to consider the squeezed surface warp tow. Tow geometry is specified, accounting for torsion of tow cross-section and crimp of weft tow path. The RVEs of the composites are geometrically assembled via a specific translational symmetry. The tensile response of the composites with the novel geometry is scrutinized utilizing a progressive damage model, compared with that of the ideal geometry. Additionally, the impact of weft tow size on the tensile response of the composites is explored.

Nano-scale modeling of energy dissipation in a single lamella reveals the significant contribution of collagen-mineral sliding to intrinsic bone toughness

Although collagen-mineral sliding is recognized as one of the intrinsic mechanisms that contribute to the bone toughness, its precise role in energy release at the nano-scale, as well as its relationship to other intrinsic toughness mechanisms, remains unclear. This lack of understanding stems from the challenges associated with quantifying the specific nature of contact at the mineral-collagen interface, and the energy dissipation capacity resulting from plastic deformation at this scale. To address this issue, we developed a three-dimensional model of mineralized collagen fibril using ABAQUS, incorporating hyperelastic isotropic collagen, elastic isotropic mineral, and a cohesive contact formulation to represent the collagen-mineral interface. Knowing the properties of collagen and mineral and using a parametric modeling approach, we estimated the properties of contact based on experimental measurements of the stress-strain behavior in a single bone lamella, considering the angle of the collagen fibril in relation to the loading axis. By comparing the experimental elastic modulus with the model predictions, we determined that the cohesive contact exhibited a maximum strength of 9 MPa and a fracture energy of 50 mJ/m2 within the bone. Further analysis indicated that fracture energy at the collagen-mineral interface is potentially closer to 5 mJ/m2. Using these findings, we concluded that the interfaces were responsible for 52% to 98% of the energy release in mineralized collagen fibrils, depending on collagen angle, both directly and indirectly by influencing stress and strain in adjacent collagen fibril. The results of this study demonstrated that plastic deformation at the collagen-mineral interface is the primary mechanism underlying bone toughness at the nano-scale. Importantly, we found that these conclusions held true regardless of the angle of the collagen fibril relative to the loading axis. The modeling framework presented in this study provides a valuable tool for investigating the elastic and post-yield behavior of bone across different mineral volume fractions at the nano-scale, and at the lamellar level.

Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

Data-driven evaluation of building materials using Ground Penetrating Radar

Ground Penetrating Radar is a widely used technology in the nondestructive evaluation and monitoring of structures during repair and maintenance phases. Within this domain, a critical focus lies in assessing layered structures such as building envelopes. Evaluating attributes like thickness and material type in layered structures traditionally demands labor-intensive manual efforts involving calculations of dielectric properties and extensive signal processing. This study addresses these challenges by leveraging machine learning data-driven models that harness the entire recorded waveform, presenting a potential breakthrough for expediting diagnostic monitoring within the construction industry. We evaluate both supervised and unsupervised machine learning models for classification and regression tasks predicting the properties of common building materials. Experimental GPR A-scans serve as input for these models, with a thorough evaluation of both instance-based and parametric modeling approaches. A detection accuracy of 100% is achieved for identifying outlier scans, while material classification accuracy is 85%. Layer thickness predictions also had a high accuracy, with a typical error of 5%. Additionally, we explore the impact of feature learning and other preprocessing strategies on model performance. Our findings demonstrate the suitability of standard data-driven models for a spectrum of supervised learning tasks for layered structure diagnostics. However, we emphasize the importance of careful attention to the distribution of training data and its relevance to intended use in the field.

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Effective comparison of two potentially crossing hazard rate curves

In survival data analysis, comparison of two hazard rate curves is critically important for evaluating a treatment effect. In many applications, the two hazard curves could potentially cross each other, violating the proportional hazards assumption in the Cox's model. In such cases, the traditional tests like the log-rank test and the Peto-Peto test that were developed based on that assumption would be ineffective. There have been some discussions in the literature on comparison of two potentially crossing hazard curves, based on either parametric modeling or nonparametric testing approaches. However, the assumed models of the existing parametric methods are often difficult to justify in practice. On the other hand, the existing nonparametric tests are usually based on the maximization with respect to an unknown crossing point, leading to complex null distributions for the corresponding test statistics. We suggest a novel method in this paper for comparing two hazard curves based on a nonparametric testing procedure. Its test statistic avoids the maximization mentioned above and consequently has the desirable asymptotic normality property under some regularity conditions. We show that the new method is effective for comparing two potentially crossing hazard curves.

Application of Multivariate Tromp Functions for Evaluating the Joint Impact of Particle Size, Shape and Wettability on the Separation of Ultrafine Particles via Flotation

Froth flotation predominantly separates particles according to their differences in wettability. However, other particle properties such as size, shape or density significantly influence the separation outcome as well. Froth flotation is most efficient for particles within a size range of about 20–200 μm, but challenges arise for very fine or coarse particles that are accompanied by low recoveries and poor selectivity. While the impact of particle size on the separation behavior in flotation is well known by now, the effect of particle shape is less studied and varies based on the investigated zone (suspension or froth) and separation apparatus used. Beyond these complexities, many particle properties are correlated, making it challenging to analyze the isolated impact of individual properties on the separation behavior. Therefore, a multidimensional perspective on the separation process, considering multiple particle properties, enhances the understanding of their collective influence. In this paper, the two-dimensional case is studied; i.e., a parametric modeling approach is applied to determine bivariate Tromp functions from scanning electron microscopy-based image data of the feed and the separated fractions. With these functions it is possible to characterize the separation behavior of particle systems. Using a model system of ultrafine (<10 μm) particles, consisting of either glass spheres or glass fragments with different wettability states as the floatable fraction and magnetite as the non-floatable fraction, allows for the investigation of the influence of descriptor vectors consisting of size, shape and wettability, on the separation. In this way, the present paper contributes to a better understanding of the complex interplay between certain descriptor vectors for the case of ultrafine particles. Furthermore, it demonstrates the benefits of using multivariate Tromp functions for evaluating separation processes and points out the limitations of SEM-based image measurements by means of mineral liberation analysis (MLA) for the studied particle size fraction.

Design for early-age structural performance of 3D printed concrete structures: A parametric numerical modeling approach

This paper presents a design framework based on structural performance of 3D concrete printing (3DCP) that considers three strength criteria for early-age failure modes: plastic collapse, elastic buckling, and flexural collapse. A simulator of structural behavior during printing was developed to take any shell or linear model, slice it into layers, perform incremental structural analysis layer by layer, and assign the respective material properties by adjusting the age of each layer when a new one is added. Then, finite element analysis for each printing stage indicates whether collapse might occur based on rheological and mechanical material properties over time. Printing experiments validated the criteria based on collapse layer estimation for a solid cylinder (plastic collapse), a thin-wall structure (elastic buckling), and an overhang structure (flexural collapse). Plastic collapse was most accurately predicted by a Drucker-Prager yield criterion for an unrestrained base and by a Modified Lade and Mohr-Coulomb criterion for a partially restrained base, while elastic buckling was predicted by linear elastic analysis. Additionally, this paper introduces flexural collapse as a distinct failure mode in early age 3DCP, proposing design criteria for overhang structures based on printing experiments. Experimental lessons learned were used to establish design constraints to aid the design of 3DCP structures, which were tested in the design of a hollow column and a cantilever structure. This study advances computational design for 3DPC structures with a new design methodology that combines constrained design and parametric numerical modeling.

SIMULATION STUDY ON PARAMETRIC MODELLING OF MAGNETORHEOLOGICAL DAMPER SYSTEM

The Magnetorheological (MR) damper system serves as a semi-active suspension system designed to absorb road disturbances. This research aims to replicate the MR damper system through a parametric modeling approach, specifically employing the Simple Bouc-Wen Model, Spencer Model, and Hyperbolic-Tangent Model. The objective of this paper is to assess the effectiveness of these modeled MR damper systems with the utilization of a Fuzzy-PID controller. The simulation process is conducted within MATLAB Simulink, whereby a block diagram is devised based on equations related to the suspension system and the parametric models. The inputs for the system encompass sinusoidal and bump road profiles. The outcomes from the modeled MR damper systems reveal that the semi-active suspension system outperforms the passive suspension system. The primary simulation metrics under consideration include sprung displacement, sprung acceleration, and unsprung acceleration. Among the three MR damper models, the Hyperbolic-tangent model displays the most favorable performance, with a mean squared error of 51.44%, while the Bouc-Wen model exhibits the least favorable performance, registering a mean squared error of 25.96%. The integration of the Fuzzy-PID controller demonstrates an enhancement in the sprung suspension system, though the sprung acceleration remains relatively unchanged.

A Parametric Modelling Approach for Energy Retrofitting Heritage Buildings: The Case of Amsterdam City Centre

The city of Amsterdam has ambitious goals to achieve a 95% reduction in carbon emissions by 2050 and to phase out natural gas by 2040. Disconnecting the building stock from natural gas requires well-ventilated and well-insulated buildings and a switch to renewable energy sources, making optimal use of heat pumps and sustainable heating solutions available locally. Most buildings in the historical city centre are protected and often insufficiently insulated, leading to increased energy use and a poor thermal environment. Standard retrofitting interventions may be restricted, requiring new approaches to balancing the need for energy efficiency and the preservation of heritage significance. With the case of the Amsterdam City Centre, the goal of this research is to present a parametric modelling approach for energy retrofitting heritage buildings and to identify minimum requirements for preparing the residential stock to lower temperature heat (LTH). Using parametric design and bottom-up energy modelling, the research estimates that a 69.1% of natural gas reduction could be achieved when upgrading the buildings to lower temperature (LT). Results of this paper also demonstrate how the applied approach can be used to guide decisions on the improvement in energy performance of the historic built environment.

Parametric investigation of ultrashort pulsed laser surface texturing on aluminium alloy 7075 for hydrophobicity enhancement

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material’s surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity.

Micromechanical simulation of interfacial fracture behavior using cohesive zone modeling for TRIP steel composite with ceramic particles

ABSTRACT In this work, ceramic particle and metal matrix interfacial delamination in transformation-induced plasticity steel composite reinforced with magnesium partially stabilized zirconia particles is investigated using a parametric modeling approach. The global behavior of the composite is modeled using elastic and Johnson-Cook plasticity models for the ceramic particles and the austenite matrix. Interfacial degradation is implemented through a cohesive zone model with a traction-separation law. Both perfect and damaged models are considered in the global stress-strain curve analysis. In the damaged model, the plastic region is characterized by softening and hardening stages, corresponding to unstable and stable crack propagation, respectively. To comprehensively identify the interfacial evolution, parameters such as normal contact strength, normal separation and stiffness degradation are evaluated along the particle/matrix interface. From a statistical perspective, the mechanical behavior of the system is analyzed through the kernel distribution plots for both the particles and the matrix. As the strain level increases, right- and left-skewed distributions are observed in the particles and matrix, respectively, particularly under high-strain conditions. Consequently, in the plastic hardening region, the median value exceeds the mean value, indicating that relying solely on the average stress value results in an underestimation during significant delamination.

Measuring Efficiency Performance in Economic Productivity Function: Parametric and Non-Parametric Frontier Approach

The parametric and non-parametric frontier approach is the famous frontier measurement efficiency in economic productivity. Many researches on economics-based has focused on efficiency. Basically, efficiency comprises two important components. First is technical efficiency and second is allocative efficiency. Combination of technical and allocative efficiency called as economic efficiency. In order to measure both components of efficiency, parametric and non-parametric approaches have been utilized to either estimate or calculate the efficiency frontier. Many studies have adopted non-parametric, deterministic approach such as Data Envelopment Analysis (DEA) that impose no functional form on the cost or production function, but parametric estimation of cost, production, or profit functions are more relevant approach in the economics-based literature. Stochastic Frontier Approach (SFA) is the most popular among parametric model approach. Deterministic frontier functions can be solved using either mathematical programming or econometric techniques, while stochastic specifications are estimated solely through econometric methods. This study’s main contribution is a review of efficiency concepts, measurement methods, and the strengths, limitations, and data requirements of parametric and non-parametric models.

A parametric approach towards semi-automated 3D as-built modeling

Building Information Modeling (BIM) has been developed in response to the growing complexity of construction projects. BIM implementation is beneficial throughout the entire building life cycle, and thus has been widely adopted in new projects. However, BIM implementation in existing buildings is impeded by the lack of as-built models. Conventionally, three-dimensional (3D) as-built BIMs are generated by experienced modelers, which is time-consuming and error-prone. To cater to the need, Scan-to-BIM is a solution for automation in as-built BIM generation. In the context of automated Scan-to-BIM, the parametric modeling process is worth investigating as it has the ability to not only reconstruct 3D objects from a variety of categories from point clouds but also offer flexibility to update objects simply by changing the values of the correlative parameters. Hence, this study proposes a semi-automated framework for assisting 3D as-built modeling through a parametric modeling approach. The presented methodology starts with wall boundary parameter extraction through 3D to 2D projection of the wall segments and line detection techniques, followed by retrieving geometric parameters of all other non-wall elements via CloudCompare. The extracted parameters are structured into a Microsoft® Excel® file and fed into Autodesk® Dynamo for 3D BIM creation using a series of designed logic. To substantiate the viability, the proposed framework is employed in two datasets containing structural, architectural, furniture, mechanical, and plumbing objects (further categorized into structural, hosted and non-hosted elements). The Intersection over Union (IoU) of structural elements was 96.35%, while the root-mean-square error (RMSE) of hosted and non-hosted elements was 3.634 and 2.607 mm, respectively. This study established a universal methodology for semi-automated 3D as-built modeling that can guide future research.

Three-dimensional mechanical characteristics analysis of bolted joints and loosening mechanism

Bolted joints are widely used in various industries. However, in challenging conditions such as shock, vibration, and temperature fluctuations, threads can loosen, leading to equipment damage and accidents. Scholars have introduced self-locking thread designs like wedge-shaped self-locking threads and stepped threads to improve anti-loosening performances. While experiments support their effectiveness, previous research primarily focused on optimizing thread structure parameters, and the studies of loosening mechanism are not in-depth enough, which restricts the development of anti-loosening bolted joints. This paper proposes a parametric modeling approach to create 3D elastic–plastic finite element models of bolted joints with different thread profiles. This approach allows for a comprehensive analysis of structural behavior, stress distribution, and load conditions, enabling the in-depth examination of the mechanical properties for thread loosening. A novel arc-lock anti-loosening threads design is proposed. Arc-lock threads are found to provide superior load distribution uniformity, increased normal force, and enhanced frictional resistance. Furthermore, the correctness of the finite element results is validated through photoelastic experiments and transverse vibration experiments. Results show that under a preload force of 225.5kN, the assembly of M24 Grade 10.9 bolts with Grade 10 nuts exhibit RMSE (Root Mean Square Error) values of 11.25, 9.02, and 8.76 for regular threads, wedge-shaped threads and arc-lock threads, respectively. Arc-lock threads demonstrate a more uniform stress distribution. In transverse vibration experiments under fully tightened conditions, regular threads loosened at the 2326th vibration cycle, while wedge-shaped self-locking threads and arc-lock threads maintained the preload percentages of 91.24% and 93.51%, respectively. These findings offer a scientific basis for understanding anti-loosening mechanisms in bolted joints and inform anti-loosening thread design.

Mechanical properties of corrugated structure with negative Poisson’s ratio and its application on energy absorbing box based on parametric modeling method

A novel corrugated structure is proposed owing to its "compression-contraction" negative Poisson’s ratio characteristics. Firstly, theoretical predictions of Poisson's ratio, elastic modulus and platform stress for corrugated structure are derived. Then, finite element models of corrugated structure are established by utilizing parametric modeling approach based on secondary development of Abaqus. Moreover, quasi-static compression test of corrugated structure is executed to verify the accuracy of theoretical predictions and parametric models. Subsequently, the influences of geometric parameters and various base material combinations on mechanical properties of corrugated structure are investigated, and the optimal base material combination Al-ABS is obtained. Afterwards, impact performances of Al-ABS corrugated structure under various impact velocities and impact angles are studied, and deformation modes, stress-strain curves and specific energy absorptionSEAare acquired. In addition, the accuracy and reliability of the FEM of energy absorbing box embedded with corrugated structure is verified through energy curves and quasi-static compression test on 3D printed energy absorbing box. Finally, multi-objective optimization design on energy absorbing box embedded with Al-ABS corrugated structure is performed with design variables of θ,r1,r2,d and optimization objectives of minimum massMs, minimum peak crushing forcePCF and maximum energy absorption EA by employing NSGA III algorithm. Compared with un-optimized corrugated structure energy absorbing box, the MsandPCFof post-optimized box decrease by 1.31%, 13.98%, accordingly, and the EA and SEA of post-optimized box increase by 10.86% and 12.33%, separately.

Impact of indoor environmental quality weighting schemes on office architectural design decisions

The importance of indoor environmental quality (IEQ) cannot be overstated, as it impacts the health and satisfaction of occupants. To evaluate overall indoor comfort, all four IEQ domains must be considered together. However, there is a lack of guidance on how to prioritize each domain and corresponding metrics, particularly during the early design stage. Therefore, this study investigates the effect of different weighting schemes on commercial building design elements to have high overall comfort. First, a parametric modeling approach for a shoe-box office building is developed to quantify IEQ domains using various design parameters. Subsequently, multiple weighting options are defined, resulting in an aggregated IEQ score. Next, high-performance designs are identified and compared based on each weighting scheme for two different climates. Results show that, as long as all four comfort domains are considered, the choice of weighting scheme does not significantly affect the selection of high-performance designs. Therefore, an equal weighting scheme is suggested for the early stage. This paper presents an original approach to support design decisions and the mutual weightings of IEQ factors.

Statistical Challenges from Trials of Potentially Curative Treatments: Validation of Cure Assumptions When Analyzing Zuma-7 Follow-up Data of Axi-Cel and Standard of Care Therapy

Introduction: ZUMA-7 is the largest randomized controlled trial (RCT) for chimeric antigen receptor (CAR) T-cell therapy which compared axicabtagene ciloleucel (Axi-cel) to standard of care (SoC) including autologous stem cell transplant (ASCT) for the treatment of relapsed or refractory DLBCL. Both arms of ZUMA-7 contained potentially curative treatments; however, differences in the treatment completion rate (94% received axi-cel and 36% received ASCT) and timing to receive definitive treatment led to differences in the extent and timing of cure. Furthermore, 57% of the SoC arm received potentially curative CAR-T therapies in subsequent lines of therapy leading to a delayed cure in that fraction of the population. The resulting event-free survival (EFS) and overall survival (OS) curves can pose challenges in interpretation and extrapolation for the purpose of simulations of long-term treatment effects and cost-effectiveness. Mixture cure modeling (MCM) has been suggested as a superior method to evaluate treatments of a curative nature to allow for part of the population to achieve long-term remission. This method has been shown to be useful both in designing and powering clinical studies of curative therapies but also for extrapolation of long-term outcomes in simulation studies. However previous iterations of this method did not account for differences in timing of curative therapies. ZUMA-7 had two pivotal analyses separated by nearly 2-years of follow-up: a primary EFS (datacut 1) and a primary OS analyses (data cut 2). The aim of this study was to use the two data cuts of ZUMA-7 to validate MCM in 2L LBCL and describe differences that may have arisen due to treatment specific timing and extent of cure. Methods: Overall survival individual patient-level data from the ZUMA-7 trial for axi-cel and SoC from both data cut 1 (median follow-up: 24.9 months) and data cut 2 (median follow-up: 47.2 months) were used in this analysis. Three different parametric modeling approaches were applied and compared in both data cuts: standard parametric model, spline-based model, and MCMs. Modeling approaches were evaluated for plausibility across a range of metrics, across the two data cuts, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates. Additionality, the ability of the early data cut extrapolation to predict the actual data was assessed by comparing the model predictions with the latest KM estimator in both trial arms. Differences between trial arms were evaluated. Results: Cure-based and spline models provided the best fit based on the percentage inaccuracy of extrapolations fitted with data cut 1 in comparison to 5-year OS Kaplan-Meier estimates confirmed by data cut 2. In the axi-cel treatment arm, the percentage inaccuracy range between 2% and 27% (Figure 1). Based on data cut 1, cure fractions ranged considerably across the MCM extrapolation forms considered for both treatment arms (range: 24-54% [Figure 2] and 35-49% for axi-cel and SoC, respectively). Notably, the range of cure fractions narrowed considerably based on extrapolations performed on the more mature data cut 2 (range: 50-54% [Figure 2] and 41-50% for axi-cel and SoC, respectively). Overall, the uncertainty in the estimate of cure fractions based on MCMs was reduced from data cut 1 to data cut 2 for axi-cel vs. SoC, suggesting clinical input is needed to validate early extrapolations using less mature data cuts. Conclusion: Our analysis suggests that MCMs are helpful in making accurate predictions of survival based on immature data when a plateau in the survival curve is clinically plausible. However, this analysis also highlights the importance of considering clinical plausibility to guide specific curve selection in the presence of immature survival data, as there was a high degree of variability in the early extrapolations which was reduced with the longer-term follow-up. The ability to reliably extrapolate from early data may improve clinical decision making and reduce delays in patient access to potentially lifesaving treatments as more mature follow-up data are being collected.