Sparse Experimental Data Research Articles

Derivation of site-specific soil-water characteristic curve (SWCC) from extremely sparse experimental data by hierarchical Bayesian method with consideration of geotechnical sites similarity

Soil-water characteristic curves (SWCCs) play a crucial role in understanding soil behavior related to water movement and soil moisture effects, rendering them an essential tool in engineering geology and geotechnical engineering applications. Traditionally, SWCCs are determined through labor-intensive laboratory experiments involving varying levels of suction, a process that can take several months. Moreover, obtaining a high-quality SWCC from numerous measurements becomes particularly challenging when immediate site-specific data are required for design and analysis. This paper introduces a hierarchical Bayesian method for deriving site-specific SWCCs by integrating extremely sparse data (e.g., one or two measurements) for the site of interest with existing data from sites with similar geological and sedimentary characteristics. The SWCC parameters are estimated using a Bayesian framework and Markov chain Monte Carlo simulations. This approach not only enables the derivation of accurate SWCCs but also helps quantify the associated uncertainties. The effectiveness of the proposed method is demonstrated using both numerical and real-world data from different types of loess and unsaturated soils in the unsaturated soil database (UNSODA). The results show that site-specific SWCCs of unsaturated soils can be accurately estimated from sparse measurements by incorporating information from similar sites. This work offers an efficient and reasonably accurate approach for deriving SWCCs of unsaturated soils for geotechnical applications, especially when the number of site-specific SWCC measurement is extremely sparse and limited.

High precision aerodynamic heat prediction method based on data augmentation and transfer learning

Data-driven modeling methods have become one of the main technologies for predicting aerodynamic heat in hypersonic conditions. However, due to the limitations of wind tunnel experimental conditions, the spatial distribution of aerothermal wind tunnel experimental data is often sparse, and the sample size is relatively small. Furthermore, there is a lack of direct correlation in the aerodynamic heat distribution data among different shapes of vehicles, which poses challenges for constructing high-performance data-driven aerodynamic heat prediction models. To address these issues, this paper proposes a high-precision aerodynamic heat modeling and prediction method based on data augmentation and transfer learning. First, integrating the concept of data fusion, we propose to enhance the sparse aerothermal wind tunnel experimental data by using deep neural networks and introducing low-precision numerical computation results. Next, based on the close physical correlation between boundary layer outer edge information and wall surface aerodynamic heat, we construct the aerodynamic heat prediction model ED-ResNet using a double-series residual neural network. Finally, by fine-tuning the ED-ResNet model for transfer learning, high-precision predictions of aerothermal wind tunnel experimental results for different shaped vehicles are achieved under small sample conditions. Verification using hypersonic double-ellipsoid, blunt cone, and blunt bicone shows that after data augmentation, the prediction error of the aerodynamic heat prediction model is significantly reduced to 1/3 of that when data augmentation is not used. Moreover, through transfer learning, the model effectively leverages existing hypersonic double-ellipsoid aerothermal wind tunnel experimental data to achieve high-precision predictions of aerodynamic heat distribution for blunt cone and blunt double cone under different incoming flow conditions, with normalized root mean square error(NRMSE) maintained below 10%.

Exploring protein structural ensembles: Integration of sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling methods.

Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure-function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in Escherichia coli. It also estimates missing kinetic parameters and reconciles them with sparse experimental data, substantially reducing parameter uncertainty and improving accuracy. This framework will be valuable for researchers studying metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnology.

Adaptive activation functions for predictive modeling with sparse experimental data

Adaptive activation functions for predictive modeling with sparse experimental data

Accurate Method for Estimating Wall-Friction Based on Analytical Wall-Law Model

A novel method is proposed for accurately determining the local wall friction through the near-wall measurement of time-average velocity profile in a Type-A turbulent boundary layer (TBL). The method is based on the newly established analytical wall-law in Type-A TBL. The direct numerical simulations (DNS) data of turbulence on a zero-pressure-gradient flat-plate (ZPGFP) is used to demonstrate the accuracy and the robustness of the approach. To verify the reliability and applicability of the method, a two-dimensional particle image velocimetry (PIV) measurement was performed in a ZPGFP TBL with a low-to-moderate Reynolds number (Re). Via utilizing the algorithm of single-pixel ensemble correlation (SPEC), the velocity profiles in the ZPGFP TBL were resolved at a significantly improved spatial resolution, which greatly enhanced the measurement accuracy and permitted us to accurately capture the near-wall velocity information. The accuracy of the approach is then quantitatively validated for the high Reynolds number turbulence using the ZPGFP TBL data. The research demonstrates that the current method can provide the precise estimation of wall friction with a mean error of less than 2%, which not only possesses the advantage of its insensitivity to the absolute wall-normal distance of the measuring point, but also its capability of providing an accurate prediction of wall shear stress based on fairly sparse experimental data on the velocity profile. The current study demonstrates that the wall shear stress can be accurately estimated by a velocity even at a single-point either measured or calculated in the near-wall region.

χc2 tensor meson transition form factors in the light front approach

We continue our work on the light-front formulation of quarkonium γ∗γ transition form factors, extending the formalism to JPC = 2++ tensor meson states. We present an analysis of γ∗γ → χc2 transition amplitude and the pertinent helicity form factors. Our relativistic formalism is based on the light-front quark-antiquark wave function of the quarkonium. We calculate the two-photon decay width as well as three independent γ∗γ transition form factors for Jz = 0, 1, 2 as a function of photon virtuality Q2. We compare our results for the two-photon decay width to the recently measured ones by the Belle and BES III collaborations. Even when including relativistic corrections, a very small Γ(λ = 0)/Γ(λ = 2) ~ 10−3 ratio is found which is beyond present experimental precision. We also present the form factors as a function of photon virtuality and compare them to the sparse experimental data on the so-called off-shell width. The formalism presented here can be used for other 2++ mesons, excited charmonia or bottomonia or even light qq¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ q\\overline{q} $$\\end{document}-mesons.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 μs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

Physics‐constrained symbolic model discovery for polyconvex incompressible hyperelastic materials

AbstractWe present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate basis functions that can be re‐parametrized with a more complex mathematical form, and (2) the resultant elasticity model is guaranteed to fulfill the polyconvexity, which ensures that the acoustic tensor remains elliptic for any deformation. To further improve the interpretability, we use genetic programming to convert each univariate basis into a compact mathematical expression. The resultant multi‐variable mathematical models obtained from this proposed framework are not only more interpretable but are also proven to fulfill physical laws. By controlling the compactness of the learned symbolic form, the machine learning‐generated mathematical model also requires fewer arithmetic operations than its deep neural network counterparts during deployment. This latter attribute is crucial for scaling large‐scale simulations where the constitutive responses of every integration point must be updated within each incremental time step. We compare our proposed model discovery framework against other state‐of‐the‐art alternatives to assess the robustness and efficiency of the training algorithms and examine the trade‐off between interpretability, accuracy, and precision of the learned symbolic hyperelastic models obtained from different approaches. Our numerical results suggest that our approach extrapolates well outside the training data regime due to the precise incorporation of physics‐based knowledge.

Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media

Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics.

Lignin structure dynamics: Advanced real-time molecular sensing strategies

Lignin, with its abundant and high energy density, offers potential for sustainable biofuels and chemicals. However, current research faces two primary challenges: limited experimental sampling frequency, impeding the real-time analysis and a lack of theoretical activation energies to elucidate thermodynamic and kinetic properties associated with structural changes under various processing conditions including reaction temperature (343 to 363 K), time (15 to 30 min), and chip size (1.0 to 5.0 mm). To address these challenges, we introduce the “real-time molecular-level sensing approach,” which considers delignification, redeposition, and de/repolymerization over time and various scales, leveraging sparse experimental data and theoretical activation energies. Our approach combines density functional theory, ab-initio molecular dynamics, and kinetic Monte Carlo simulations to deeply explore dynamic nature of lignin. Specifically, we gain insights into lignin content within biomass, molecular weight distribution, and monolignol ratio, improving lignin’s utilization during processing. This work establishes a new standard for comprehensive lignin analysis, fostering efficient lignin utilization by tracking their properties at the molecular level in the real-time manner.

Immature porcine cortical bone mechanical properties and composition change with maturation and displacement rate

Computational models of mature bone have been used to predict fracture; however, analogous study of immature diaphyseal fracture has not been conducted due to sparse experimental mechanical data. A model of immature bone fracture may be used to aid in the differentiation of accidental and non-accidental trauma fractures in young, newly ambulatory children (0–3 years). The objective of this study was to characterize the evolution of tissue-level mechanical behavior, composition, and microstructure of maturing cortical porcine bone with uniaxial tension, Raman spectroscopy, and light microscopy as a function of maturation. We asked: 1) How do the monotonic uniaxial tensile properties change with maturation and displacement rate; 2) How does the composition and microstructure change with maturation; and 3) Is there a correlation between composition and tensile properties with maturation? Elastic modulus (p < 0.001), fracture stress (p < 0.001), and energy absorption (p < 0.014) increased as a function of maturation at the quasistatic rate by 110%, 86%, and 96%, respectively. Fracture stress also increased by 90% with maturation at the faster rate (p = 0.001). Fracture stress increased as a function of increasing displacement rate by 28% (newborn p = 0.048; 1-month p = 0.004; 3-month p= < 0.001), and fracture strain decreased by 68% with increasing displacement rate (newborn p = 0.002; 1-month p = 0.036; 3-month p < 0.001). Carbonate-to-phosphate ratio was positively linearly related to elastic modulus, and fracture stress was positively related to carbonate-to-phosphate ratio and matrix maturation ratio. The results of this study support that immature bone is strain-rate dependent and becomes more brittle at faster rates, contributing to the foundation upon which a computational model can be built to evaluate immature bone fracture.

Thermal Conductivity of Glycerol at Atmospheric Pressure Between 268 K and 363 K by Using a Steady-State Parallel-Plate Instrument

The present work reports experimental data for the thermal conductivity of glycerol which is an important fluid in many technical applications. Measurements were performed in an absolute way at ambient pressure using a steady-state guarded parallel-plate instrument (GPPI) with an average expanded (k = 2) measurement uncertainty of 2.3%. For data representation over a temperature range from (268.15 to 363.15) K in steps of 5 K, the thermal conductivities are averaged from measurements at three different temperature gradients for each temperature. The present results indicate an almost constant thermal conductivity of glycerol over the studied temperature range and agree with the sparse experimental data available in the literature. Based on the experimental database including the results from this work, a simple correlation for the thermal conductivity of glycerol at 0.1 MPa as a function of temperature between (268 and 413) K is suggested. The additional study on the influence of water as possible contamination up to water mass fractions of 0.02 on the thermal conductivity of glycerol reveals negligible changes. Overall, the experimental results from this work contribute to an improved data situation for the thermal conductivity of glycerol, particularly in the subcooled liquid region at temperatures below 283 K.

Unlocking the potential: analyzing 3D microstructure of small-scale cement samples from space using deep learning

Due to the prohibitive cost of transporting raw materials into Space, in-situ materials along with cement-like binders are poised to be employed for extraterrestrial construction. A unique methodology for obtaining microstructural topology of cement samples hydrated in microgravity environment at the International Space Station (ISS) is presented here. Distinctive Scanning Electron Microscopy (SEM) micrographs of hardened tri-calcium silicate (C3S) samples were used as exemplars in a deep learning-based microstructure reconstruction framework. The proposed method aids in generation of an ensemble of microstructures that is inherently statistical in nature, by utilizing sparse experimental data such as the C3S samples hydrated in microgravity. The hydrated space-returned samples had exhibited higher porosity content (~70 %) with the portlandite phase assuming an elongated plate-like morphology. Qualitative assessment of the volumetric slices from the reconstructed volumes showcased similar visual characteristics to that of the target 2D exemplar. Detailed assessment of the reconstructed volumes was carried out using statistical descriptors, and was further compared against micro-CT virtual data. The reconstructed volumes captured the unique microstructural morphology of the hardened C3S samples of both space-returned and ground-based samples, and can be directly employed as Representative Volume Element (RVE) to characterize mechanical/transport properties.

Geographical and taxonomic patterns in aerobic traits of marine ectotherms.

The metabolism and hypoxia tolerance of marine ectotherms play key roles in limiting species geographical ranges, but underlying traits have only been directly measured for a small fraction of biodiversity. Here we diagnose and analyse spatial and phylogenetic patterns in hypoxia tolerance and its temperature sensitivity at ecologically active metabolic rates, by combining a model of organismal oxygen (O2) balance with global climate and biogeographic data for approximately 25 000 animal species from 13 phyla. Large-scale spatial trait patterns reveal that active hypoxia tolerance is greater and less temperature sensitive among tropical species compared to polar ones, consistent with sparse experimental data. Species energetic demands for activity vary less with temperature than resting costs, an inference confirmed by available rate measurements. Across the tree of life, closely related species share similar hypoxia traits, indicating that evolutionary history shapes physiological tolerances to O2 and temperature. Trait frequencies are highly conserved across phyla, suggesting the breadth of global aerobic conditions selects for convergent trait diversity. Our results support aerobic limitation as a constraint on marine habitat distributions and their responses to climate change and highlight the under-sampling of aerobic traits among species living in the ocean's tropical and polar oxythermal extremes. This article is part of the theme issue 'The evolutionary significance of variation in metabolic rates'.

Modelling clogging dynamics in groundwater systems using multiscale homogenized physics informed neural network (MHPINN)

Groundwater systems, or also commonly known as large-scale closed and saturated porous media systems in general, are important sources of potable water, especially in arid countries. However, maintenance of groundwater systems is operationally expensive and time-consuming, hence requiring field engineers to optimally predict the system’s breakthrough phase. To address this engineering challenge, this study developed an alternative physics informed machine learning model, termed as multiscale homogenized physics informed neural network (MHPINN), to model and predict the effluent discharge concentrations from closed and saturated porous media systems when removing different contaminants. The proposed MHPINN model mainly exploits the homogenization theory, coupled with multiscale perturbation analysis, for hydrodynamic modelling to extract important model input features pertaining to the pore-scale clogging behavior in porous media. Together with available experimental data, the extracted model features then set the input layer for training neural network model to predict the discharge concentration of emulated porous media groundwater systems. At the same time, the model training is performed with modified mean square error (MSE) cost function, based upon homogenized mathematical representations derived from a series of rigorous mathematical constructs and formulations, to better facilitate the model’s learning, especially under sparse data constraint. To verify the proposed methodology, MPINN is trained (and validated) with data obtained from five historical experimental studies, which emulated porous media systems to remove ammonium ions and fine-scaled colloids, to model and predict the discharge concentration in each study. Overall, MHPINN outperformed other conventional machine learning models (random forest, support vector machines, etc.) by obtaining higher predictive accuracy of around 4% on average from the model’s validation step across the five different experimental studies. Therefore, the predictive analysis underscored the effectiveness of MHPINN to combine both physics-based modelling and machine learning to model complex porous media systems, under sparse experimental data constraints, hence demonstrating the potential of the proposed methodology to extend to other physical systems.

Ensemble predictions of laser ignition with a hybrid stochastic physics-embedded deep-learning framework

When investigating stochastic ignition phenomena, high-fidelity simulations and experiments for statistical characterization can be a laborious endeavor. Machine learning (ML), in particular deep-learning, offers an accurate and cost-effective approach for reduced-order modeling of forced ignition. In this work, we investigate the potential and limitations of ML in modeling ignition, especially in the presence of limited data and multiple ignition modes. To this end, we introduce a hybrid stochastic physics-embedded deep-learning framework that combines sparse experimental data from Schlieren measurements with inert large-eddy simulations for predicting the spatio-temporal evolution of ignition kernel location and morphology. This model combines a stochastic differential equation for modeling kernel dynamics and a deep-learning model for representing kernel morphology. We evaluate this modeling approach for laser ignition of a gaseous methane/oxygen (CH4/O2) model rocket combustor, where morphological effects of the ignition kernel significantly influence ignition behavior. Results demonstrate that this model can reasonably capture behavior associated with the three dominant ignition modes, namely direct, indirect, and failed ignition, along with statistics associated with kernel growth and position, at lower computational costs than high-fidelity reacting simulations. In addition, we demonstrate that this modeling framework can be employed for generating spatially resolved ignition probability maps by incorporating physics to represent kernel interaction with the turbulent jet. We note that limitations in accuracy can be observed when predicting with vastly out-of-distribution data. Nevertheless, these results demonstrate that this physics-embedded ML approach can statistically characterize forced ignition in a more cost-effective manner than reacting high-fidelity simulations, as long as sufficiently representative data is available.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

Analysing the effects of heating and gas puffing in Proto-MPEX helicon and auxiliary heated plasmas *

The Material Plasma Exposure Experiment (MPEX) is being constructed at Oak Ridge National Laboratory to investigate critical fusion reactor issues, such as plasma–material interactions (PMIs) under reactor-relevant conditions and time scales. The linear device Proto-MPEX was used as a test bed to address anticipated research and development issues associated with heating scenarios and establish the physics basis for MPEX. The SOLPS-ITER code suite has been applied to understand plasma and neutral transport in Proto-MPEX and to increase confidence in predictive simulations for MPEX. Coupling between COMSOL and SOLPS is performed to implement a 2D electron heating profile of the helicon source. The simulations show reasonable agreement with the experimental data for plasma with helicon and auxiliary electron cyclotron heating (ECH). Both Bohm and constant diffusion (D: 0.5 m2 s−1 and : 1 m2 s−1) simulations show similar levels of agreement with respect to the sparse experimental data available, assuming a few per cent impurity concentration. ECH significantly increases the target electron temperature, however, the target electron density is reduced compared to helicon-only heated plasmas due to an increase in flow velocity and radial losses. The simulations show that further increasing the ECH power results in an increase in the target electron density due to increased recycling flux and ionization. The results indicate that ECH significantly enhances the target heat flux, with ECH power of 50 kW increasing the target heat flux from 0.4 to 17 MW m−2. It is found that a small amount of gas puffing (GP) near the target plate can further increase the target heat fluxes at the higher ECH power cases, but the target heat flux is reduced at higher GP conditions due to a significant reduction in electron temperature via radiation. ECH and GP scenarios can generate a higher target flux, facilitating improved PMI studies with more reactor-relevant plasma conditions.