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At least 973 records · Page 54

Outcomes for Spine Stereotactic Body Radiation Therapy and an Analysis of Predictors of Local Recurrence

Purpose: To investigate local control, survival outcomes, and predictors of local relapse for patients treated with spine stereotactic body radiation therapy. Methods and Materials: We reviewed the records of 332 spinal metastases consecutively treated with stereotactic body radiation therapy between 2002 and 2012. The median follow-up for all living patients was 33 months (range, 0-111 months). Endpoints were overall survival and local control (LC); recurrences were classified as either in-field or marginal. Results: The 1-year actuarial LC and overall survival rates were 88% and 64%, respectively. Patients with local relapses had poorer dosimetric coverage of the gross tumor volume (GTV) compared with patients without recurrence (minimum dose [Dmin] biologically equivalent dose [BED] 23.9 vs 35.1 Gy, P<.001; D98 BED 41.8 vs 48.1 Gy, P=.001; D95 BED 47.2 vs 50.5 Gy, P=.004). Furthermore, patients with marginal recurrences had poorer prescription coverage of the GTV (86% vs 93%, P=.01) compared with those with in-field recurrences, potentially because of more upfront spinal canal disease (78% vs 24%, P=.001). Using a Cox regression univariate analysis, patients with a GTV BED Dmin ≥33.4 Gy (median dose) (equivalent to 14 Gy in 1 fraction) had a significantly higher 1-year LC rate (94% vs 80%, P=.001) compared with patients with a lower GTV BED Dmin; this factor was the only significant variable on multivariate Cox analysis associated with LC (P=.001, hazard ratio 0.29, 95% confidence interval 0.14-0.60) and also was the only variable significant in a separate competing risk multivariate model (P=.001, hazard ratio 0.30, 95% confidence interval 0.15-0.62). Conclusions: Stereotactic body radiation therapy offers durable control for spinal metastases, but there is a subset of patients that recur locally. Patients with local relapse had significantly poorer tumor coverage, which was likely attributable to treatment planning directives that prioritized the spinal cord constraints over tumor coverage. When possible, we recommend maintaining a GTV Dmin above 14 Gy in 1 fraction and 21 Gy in 3 fractions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Predicting the viscoplastic response of a crystallizing fluoropolymer using transient network theory

We employ a molecular theory of dynamic polymer networks to describe the viscoplastic response of rubbery FK-800, a thermoplastic copolymer of chlorotrifluoroethylene and vinylidene fluoride, over a broad range of thermal histories. The kinetics of crystallization at different annealing temperatures was modeled using a modified Avrami equation, whose parameters were found to evolve through simple relationships over the full temperature range of the rubbery state. By fitting experimental compression data, we discovered predictable trends for the physical parameters in our mechanical model over its full range of crystallinities (up to ≈20%) and provided insights based on molecular-level physics to justify them. Using this, an end-to-end model was developed to predict the yielding and post-yield behavior of rubbery FK-800 for arbitrary thermal histories. The model successfully predicted the highly nonlinear evolution of characteristic mechanical signatures (stiffness, yield point, post-yield drop) throughout the crystallization process. A statistical analysis of variance test was employed to determine that the measured variations in the mechanical behavior of rubbery FK-800 are primarily dictated by its fractional crystallinity, regardless of its exact thermal history.

36 MATERIALS SCIENCE↗

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).

59 BASIC BIOLOGICAL SCIENCES↗

An assessment of total depletion uncertainties for a GBC-32 dry storage cask with PLUS7 fuels using Monte Carlo sampling and two-way ANOVA F-test

It is important to accurately evaluate depletion uncertainties that exist due to the limitations of a computer code on accuracy in a nuclear depletion calculation for criticality safety analysis. In this work, the bias and bias uncertainty in the effective neutron multiplication factor (k{sub eff}) for the generic 32 PWR-assembly burnup credit (GBC-32) cask system were assessed using the Monte Carlo uncertainty sampling method for three different burnups and the isotopic biases and uncertainties obtained with the calculated and measured isotopic concentrations. The PLUS7 used nuclear fuel assemblies discharged from the Hanbit Nuclear Power Plant Unit 3 were assumed to be loaded into the GBC-32 cask. In addition, the statistical F-test in the two-way analysis of variance (ANOVA) was suggested to efficiently estimate the statistically converged total depletion uncertainty in k{sub eff}, with a significant reduction of computing efforts in the Monte Carlo criticality calculations, and it was applied to the GBC-32 cask system after a sensitivity analysis was performed to select the important nuclides having significant contributions to the total depletion uncertainty. With the statistical F-tests in the two-way ANOVA, 100 criticality calculations were needed to get a statistically converged total depletion uncertainty for 10 and 30 GWD/MTU, while 120 criticality calculations were needed for 50 GWD/MTU. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Descriptors of water aggregation

For this work, we rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (r OH ) and HOH bond angle (θ HOH ), (ii) two-body: OO distance (r OO ), OHO angle (θ OHO ), and HOOX dihedral angle ($\phi$ HOOX ), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θ OOO ), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (q m , m = 2, 3, 4, 5) and radius of gyration (R g ). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H 2 O) n , n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that r OH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE Canopy ). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE Canopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE Canopy . The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

54 ENVIRONMENTAL SCIENCES↗

Dosimetric characterization of the GammaClip™{sup 169}Yb low dose rate permanent implant brachytherapy source for the treatment of nonsmall cell lung cancer postwedge resection

Purpose: A novel {sup 169}Yb low dose rate permanent implant brachytherapy source, the GammaClip™, was developed by Source Production and Equipment Co. (New Orleans, LA) which is designed similar to a surgical staple while delivering therapeutic radiation. In this report, the brachytherapy source was characterized in terms of “Dose calculation for photon-emitting brachytherapy sources with average energy higher than 50 keV: Report of the AAPM and ESTRO” by Perez-Calatayud et al. [Med. Phys. 39, 2904–2929 (2012)] using the updated AAPM Task Group Report No. 43 formalism.Methods: Monte Carlo calculations were performed using Monte Carlo N-Particle 5, version 1.6 in water and air, the in-air photon spectrum filtered to remove photon energies below 10 keV in accordance with TG-43U1 recommendations and previously reviewed {sup 169}Yb energy cutoff levels [D. C. Medich, M. A. Tries, and J. M. Munro, “Monte Carlo characterization of an Ytterbium-169 high dose rate brachytherapy source with analysis of statistical uncertainty,” Med. Phys. 33, 163–172 (2006)]. TG-43U1 dosimetric data, including S{sub K}, D-dot (r,θ), Λ, g{sub L}(r), F(r, θ), φ{sub an}(r), and φ{sub an} were calculated along with their statistical uncertainties. Since the source is not axially symmetric, an additional set of calculations were performed to assess the resulting axial anisotropy.Results: The brachytherapy source's dose rate constant was calculated to be (1.22 ± 0.03) cGy h{sup −1} U{sup −1}. The uncertainty in the dose to water calculations, D-dot (r,θ), was determined to be 2.5%, dominated by the uncertainties in the cross sections. The anisotropy constant, φ{sub an}, was calculated to be 0.960 ± 0.011 and was obtained by integrating the anisotropy factor between 1 and 10 cm using a weighting factor proportional to r{sup −2}. The radial dose function was calculated at distances between 0.5 and 12 cm, with a maximum value of 1.20 at 5.15 ± 0.03 cm. Radial dose values were fit to a fifth order polynomial and dual exponential regression. Since the source is not axially symmetric, angular Monte Carlo calculations were performed at 1 cm which determined that the maximum azimuthal anisotropy was less than 8%.Conclusions: With a higher photon energy, shorter half-life and higher initial dose rate {sup 169}Yb is an interesting alternative to {sup 125}I for the treatment of nonsmall cell lung cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Asymptotic inconsistency of the cumulative algorithm for laser-induced damage probability analysis

The “cumulative algorithm” is a data analysis method that has been proposed to provide an objective, nonparametric determination of laser-induced damage probability as a function of fluence from experimental data that contain both damaged sites and undamaged sites (i.e., 1-on-1 or S-on-1 testing protocols). In this work, the limitations of this approach are explored by considering the asymptotic limit of a large number of test sites. It is shown that the cumulative algorithm does not converge to the true probability distribution and significantly underestimates the damage probability near the damage onset. Here, based on the results of this work, the cumulative algorithm is not recommended for accurate estimation of damage probability.

Computational methods↗

Method of Correlation Coefficient Optimization Used in UV-vis Spectrophotometric Analysis for Effluent in Catalytic Ozonation

A wavelength selection method for spectrophotometric analysis, named correlation coefficient optimization (CCO), was employed for reagent-free analysis of organics (humic acid) in real sewage. The modeling set of CCO uses the effluent in the ozonation process of humic acid (HA) solution. In order to verify the relationship between HA concentration and absorbance at the selected wavelength, HA solutions with different mass concentrations were prepared as a validation set. The study showed that the absorbance at 300 nm could be a good quantitative indication of HA concentration. In addition, the absorbance at 300 nm was applied to analyze the HA concentration in the ozonation process. The decomposition of HA fits the pseudo-first-order kinetic model and has a linear relationship with pH and (T – 20){sup 2} (T is reaction temperature).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evolution of intermittent filaments in the scrape-off layer of NSTX

Filamentary structures naturally arise from background turbulence in the scrape-off layer (SOL) of plasmas, leading to significant particle and heat transport that can degrade overall plasma confinement. This enhanced transport can contribute to unacceptably high heat loads on plasma-facing components. As such, understanding the physics of SOL plasma filaments is critical for predicting and mitigating their effects in future fusion devices. On the National Spherical Torus Experiment (NSTX), plasma filaments—commonly referred to as “blobs”—were investigated using the gas-puff imaging (GPI) diagnostic in the edge and SOL regions. The analysis involved identifying, segmenting, and tracking the characteristic contours of the blobs in each frame of the GPI video sequences. Their evolution was characterized through shape descriptors, velocity, and angular velocity derived from their contour coordinates. The results indicate that as the blob area increases, their shapes become more concave and less circular, suggesting reduced structural stability in larger blobs. This result aligns with previous theoretical results where it was shown that larger blobs are more susceptible to instabilities [Krasheninnikov et al., J. Plasma Phys. 74, 679–717 (2008) and D'Ippolito et al., Phys. Plasmas 18, 060501 (2011)]. A positive correlation was observed between radial velocity and radial position, suggesting radially outward acceleration of the filaments, potentially driven by decreasing viscous drag toward the far SOL. Interestingly, blobs in background SOL turbulence exhibited minimal spinning in contrast to filaments originating from edge localized modes, which show substantial rotation during their paths [Lampert et al., Phys. Plasmas 29, 102502 (2022)]. Statistical analysis of the solidity and total curvature shape descriptors, along with their temporal evolution, revealed relatively broad, near-Gaussian distributions. This suggests that blob morphology is strongly influenced by stochastic turbulent processes in the surrounding plasma environment. Blob parameters were also compared with bulk plasma and radial profile measurements. Notable trends were found between blob rotation and poloidal velocity with collisionality and line-integrated density. These findings contribute to a deeper understanding of blob dynamics and provide valuable insights for refining SOL turbulence models.

Covariance and correlation↗

Parametric and Nonparametric Models of U.S. Cost Overruns for Nuclear Power Plants

This study presents new data-driven models to estimate the effect of capacity on the percentage of cost overruns in the United States for nuclear power plant construction projects before and after the Three Mile Island accident. Parametric and nonparametric models have been developed that describe the significant shifts in nuclear energy costs during the dynamic environment. Employing a contemporary descriptive methodology and a quantitative analysis, we furnish a comprehensive overview of the alterations in cost overrun distribution and show the changes observed in other pivotal metrics alongside cost overruns. Our emphasis lies in documenting the fluctuations in cost overruns alongside nuclear reactor capacity levels and the increase of the overnight capital costs to build nuclear reactors. Our results show that increasing the size of nuclear reactors is not a factor statistically significant to decrease the percentage of cost overruns, and the probit model results provide evidence that an increase in size increases the probability of having cost overruns larger than 100% (double the estimated cost). We also compare our findings to two other regions: Asia and Europe.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A semi-automatic method for left ventricle volume estimate: an in vivo validation study

This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.

NASA Discipline Cardiopulmonary↗

Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

NASA Discipline Cardiopulmonary↗

Cross-calibration of liquid and solid QCT calibration standards: corrections to the UCSF normative data

Quantitative computed tomography (QCT) has been shown to be a precise and sensitive method for evaluating spinal bone mineral density (BMD) and skeletal response to aging and therapy. Precise and accurate determination of BMD using QCT requires a calibration standard to compensate for and reduce the effects of beam-hardening artifacts and scanner drift. The first standards were based on dipotassium hydrogen phosphate (K2HPO4) solutions. Recently, several manufacturers have developed stable solid calibration standards based on calcium hydroxyapatite (CHA) in water-equivalent plastic. Due to differences in attenuating properties of the liquid and solid standards, the calibrated BMD values obtained with each system do not agree. In order to compare and interpret the results obtained on both systems, cross-calibration measurements were performed in phantoms and patients using the University of California San Francisco (UCSF) liquid standard and the Image Analysis (IA) solid standard on the UCSF GE 9800 CT scanner. From the phantom measurements, a highly linear relationship was found between the liquid- and solid-calibrated BMD values. No influence on the cross-calibration due to simulated variations in body size or vertebral fat content was seen, though a significant difference in the cross-calibration was observed between scans acquired at 80 and 140 kVp. From the patient measurements, a linear relationship between the liquid (UCSF) and solid (IA) calibrated values was derived for GE 9800 CT scanners at 80 kVp (IA = [1.15 x UCSF] - 7.32).(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Program Space Physiology and Countermeasures↗

Moisture and Temperature Influences on Nonlinear Vegetation Trends in Serengeti National Park

While long-term vegetation greening trends have appeared across large land areas over the late 20th century, uncertainty remains in identifying and attributing finer-scale vegetation changes and trends, particularly across protected areas. Serengeti National Park (SNP) is a critical East African protected area, where seasonal vegetation cycles support vast populations of grazing herbivores and a host of ecosystem dynamics. Previous work has shown how non-climate drivers (e.g. land use) shape the SNP ecosystem, but it is still unclear to what extent changing climate conditions influence SNP vegetation, particularly at finer spatial and temporal scales. We fill this research gap by evaluating long-term (1982–2016) changes in SNP leaf area index (LAI) in relation to both temperature and moisture availability using Ensemble Empirical Mode Decomposition and Principal Component Analysis with regression techniques. We find that SNP LAI trends are nonlinear, display high sub-seasonal variation, and are influenced by lagged changes in both moisture and temperature variables and their interactions. LAI during the long rains (e.g. March) exhibits a greening-to-browning trend reversal starting in the early 2000s, partly due to antecedent precipitation declines. In contrast, LAI during the short rains (e.g. November, December) displays browning-to-greening alongside increasing moisture availability. Rising temperature trends also have important, secondary interactions with moisture variables to shape these SNP vegetation trends. Our findings show complex vegetation-climate interactions occurring at important temporal and spatial scales of the SNP, and our rigorous statistical approaches detect these complex climate-vegetation trends and interactions, while guarding against spurious vegetation signals.

Moisture↗

Flash x-ray radiography analysis of detonation wave propagation in additive-manufactured high explosives

Recent research has demonstrated that additive manufacturing (AM) can be used to produce directionally sensitive high explosives (HEs), but detonation wave propagation in AM HEs with variable internal structure has not been studied. In this work, samples were printed using a 73 wt. % octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine-based HE ink. Flash x-ray radiography imaging was used to observe density changes in two imaging planes of each sample during detonation, and high-speed imaging was used to calculate the detonation velocity at the HE surface. The detonation front initially appears to fail in internal channel regions of the HE, but late reactions occurred in two samples, which increased the material bulk density by 2.8%, possibly due to shock convergence phenomena. The calculated detonation wave pressure similarly increased, but the accuracy of the results is uncertain because of the deviation of the low bulk density printed samples from the cast charge principal isentrope. Here, this work demonstrates that structure can be effectively utilized to guide detonation wave propagation through HE charges.

3D printing↗

Modeling the effects of light, carbon dioxide, and temperature on the growth of potato

This study examined the effects of light, temperature and carbon dioxide on the growth of potato (Solanum tuberosum L.) in a controlled environment in order to ascertain the best growing conditions for potato in life support systems in space. 'Norland' and 'Russet Burbank' were grown in 6-L pots of peat-vermiculite for 56 d in growth chambers at the University of Wisconsin Biotron. Environmental factor levels included continuous light (24-h photoperiod) at 250, 400, and 550 micromoles m-2 s-1 PPF; constant temperature at 16, 20, and 24 degrees C; and CO2 at approximately 400, 1000, and 1600 microliters L-1. Separate effects analysis and ridge analysis provided a means to examine the effects of individual environmental factors and to determine combinations of factors that are expected to give the best increases in yields over the central design point. The response surface of Norland indicated that tuber yields were highest with moderately low temperature (18.7 degrees C), low CO2 (400 microliters L-1) and high light (550 micromoles m-2 s-1 PPF). These conditions also favored shorter stem growth. Russet Burbank tuber yields were highest at moderately low temperature (17.5 degrees C), high CO2 (1600 microliters L-1) and medium analyses will be used to project the most efficient conditions for growth of potatoes in closed ecological life support systems (CELSS) in space colonies.

NASA Discipline Life Support Systems↗

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗