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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Impedance Discontinuity Reduction Between High-Speed Differential Connectors and PCB Interfaces

High-speed serial communication (i.e., Gigabit Ethernet) requires differential transmission and controlled impedances. Impedance control is essential throughout cabling, connector, and circuit board construction. An impedance discontinuity arises at the interface of a high-speed quadrax and twinax connectors and the attached printed circuit board (PCB). This discontinuity usually is lower impedance since the relative dielectric constant of the board is higher (i.e., polyimide approx. = 4) than the connector (Teflon approx. = 2.25). The discontinuity can be observed in transmit or receive eye diagrams, and can reduce the effective link margin of serial data networks. High-speed serial data network transmission improvements can be made at the connector-to-board interfaces as well as improving differential via hole impedances. The impedance discontinuity was improved by 10 percent by drilling a 20-mil (approx. = 0.5-mm) hole in between the pin of a differential connector spaced 55 mils (approx. = 1.4 mm) apart as it is attached to the PCB. The effective dielectric constant of the board can be lowered by drilling holes into the board material between the differential lines in a quadrax or twinax connector attachment points. The differential impedance is inversely proportional to the square root of the relative dielectric constant. This increases the differential impedance and thus reduces the above described impedance discontinuity. The differential via hole impedance can also be increased in the same manner. This technique can be extended to multiple smaller drilled holes as well as tapered holes (i.e., big in the middle followed by smaller ones diagonally).

Navidi, Sal↗
DOE OSTI2025

Differential equations for cosmological correlators

Cosmological fluctuations retain a memory of the physics that generated them in their spatial correlations. The strength of correlations varies smoothly as a function of external kinematics, which is encoded in differential equations satisfied by cosmological correlation functions. In this work, we provide a broader perspective on the origin and structure of these differential equations. As a concrete example, we study conformally coupled scalar fields in a power-law cosmology. The wavefunction coefficients in this model have integral representations, with the integrands being the product of the corresponding flat-space results and “twist factors” that depend on the cosmological evolution. Similar twisted integrals arise for loop amplitudes in dimensional regularization, and their recent study has led to the discovery of rich mathematical structures and powerful new tools for computing multi-loop Feynman integrals in quantum field theory. The integrals of interest in cosmology are also part of a finite-dimensional basis of master integrals, which satisfy a system of first-order differential equations. We develop a formalism to derive these differential equations for arbitrary tree graphs. The results can be represented in graphical form by associating the singularities of the differential equations with a set of graph tubings. Upon differentiation, these tubings grow in a local and predictive fashion. In fact, a few remarkably simple rules allow us to predict — by hand — the equations for all tree graphs. While the rules of this “kinematic flow” are defined purely in terms of data on the boundary of the spacetime, they reflect the physics of bulk time evolution. We also study the analogous structures in tr ϕ 3 theory, and see some glimpses of hidden structure in the sum over planar graphs. This suggests that there is an autonomous combinatorial or geometric construction from which cosmological correlations, and the associated spacetime, emerge.

Cosmological models↗
DOE OSTI2018

Promoting hepatogenic differentiation of human mesenchymal stem cells using a novel laminin-containing gelatin cryogel scaffold

Highlights: • The gelatin cryogel was optimized for stability in cell culture medium during stem cell differentiation. • The optimized gelatin cryogel improved with the laminin, the main component of basal lamina. • The novel scaffold with laminin efficiently enhanced differentiation of mesenchymal stem cells toward hepatocyte lineage. Liver transplantation is the only definitive treatment currently available for acute and chronic liver failure. However, this approach has been restricted by complications including rejection and infection. Tissue engineering approaches using stem cell-derived functional hepatic cells offer a potential alternative. Using biologically compatible scaffolds is an important complementary key to achieve optimal construct for hepatic replacement. In the present study, to optimize the differentiation of human adipose-derived mesenchymal stem cells (ADMSCs) toward hepatocyte-like cells, a previously described gelatin cryogel was optimized and improved by laminin, the major component of basal lamina. The ADMSCs seeded on the scaffold displayed increased attachment in the presence of laminin and the MTT assay showed good compatibility for cell proliferation. The differentiation of stem cells were evaluated using glycogen staining, urea secretion measurement, hepatocyte specific cell surface analysis and gene expression analysis. The results of tests indicated that laminin protein and gelatin cryogel 3D scaffold, each on its own, enhanced hepatogenic differentiation of ADMSCs. However, when laminin immobilized on the gelatin cryogel surface, the differentiation was promoted significantly and the resulting cells showed striking similarity to HepG2 in terms of expressing studied hepatocyte markers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Adenylate cyclase 7 regulated by miR-192 promotes ATRA-induced differentiation of acute promyelocytic leukemia cells

Highlights: • AC7 affects cAMP production in APL cells. • Knockdown of AC7 inhibits ATRA-induced differentiation of APL cells. • MiR-192 directly targets 3′ UTR of AC7 mRNA and inhibits its expression. • MiR-192 knockdown promotes ATRA-induced differentiation of APL cells by up-regulating AC7. • AC7 and miR-192 expression levels are associated with the relapse of APL patients. Adenylate cyclase 7 (AC7) has been reported to participate in various biological processes during cancer progression. However, the roles of AC7 in all-trans retinoic acid (ATRA)-induced differentiation of acute promyelocytic leukemia (APL) cells are still unknown. In this study, firstly, our results showed that AC7 affected intracellular cAMP level and influenced ATRA-induced differentiation of APL cells. Secondly, we revealed that miR-192 could directly target AC7 expression and knockdown of miR-192 promoted ATRA-induced APL cell differentiation by regulating AC7 expression. Furthermore, we found that AC7 expression was lower in patients with relapsed APL than that in patients with newly diagnosed APL, while miR-192 expression was relatively higher in patients with relapsed APL. Taken together, our results show that miR-192-mediated AC7 could play important roles in differentiation of APL cells, AC7 and miR-192 might be new biomarkers and therapeutic targets for patients with relapsed APL.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

WeakIdent: Weak formulation for identifying differential equation using narrow-fit and trimming

Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of linear and nonlinear terms multiplied by a coefficient vector. This product is equal to the time derivative term, and thus generates dynamical behaviors. The goal is to identify the correct terms that form the equation to capture the dynamics of the given data. We propose a general and robust framework to recover differential equations using a weak formulation with two new mechanisms, narrow-fit and trimming, for both ordinary and partial differential equations (ODEs and PDEs). The weak formulation facilitates an efficient and robust way to handle noise, and two new mechanisms, narrow-fit and trimming, improve the coefficient support and value recoveries respectively. For each sparsity level, Subspace Pursuit is utilized to find an initial set of support from the large dictionary. Then, we focus on highly dynamic regions (rows of the feature matrix), and error normalize the feature matrix in the narrow-fit step. The support is further updated via trimming the terms that contribute the least. Finally, the support set of features with the smallest Cross-Validation error is chosen as the result. A comprehensive set of numerical experiments are presented for both systems of ODEs and PDEs with various noise levels. The proposed method gives a robust recovery of the coefficients, and a significant denoising effect which can handle up to 100% noise-to-signal ratio for some equations. We compare the proposed method with several state-of-the-art algorithms for the recovery of differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Automatic Differentiation for Adjoint Stencil Loops

Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automatic differentiation, also known as algorithmic differentiation, autodiff, adjoint differentiation, or back-propagation, is sometimes used to obtain gradients of programs that contain stencil loops. Unfortunately, conventional automatic differentiation results in a memory access pattern that is not stencil-like and not easily parallelisable. In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives. The generated loops can be parallelised and optimised for performance in the same way and using the same tools as the original computation. We have implemented this new technique in the Python tool PerforAD, which we release with this paper along with test cases derived from seismic imaging and computational fluid dynamics applications.

Automatic Differentiation↗
DOE OSTI2022

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

This article presents our work toward correct and efficient automatic differentiation of OpenMP parallel worksharing loops in forward and reverse mode. Automatic differentiation is a method to obtain gradients of numerical programs, which are crucial in optimization, uncertainty quantification, and machine learning. The computational cost to compute gradients is a common bottleneck in practice. For applications that are parallelized for multicore CPUs or GPUs using OpenMP, one also wishes to compute the gradients in parallel. Here, we propose a framework to reason about the correctness of the generated derivative code, from which we justify our OpenMP extension to the differentiation model. We implement this model in the automatic differentiation tool Tapenade and present test cases that are differentiated following our extended differentiation procedure. Performance of the generated derivative programs in forward and reverse mode is better than sequential, although our reverse mode often scales worse than the input programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

New Role of P. brasiliensis α-Glucan: Differentiation of Non-conventional Dendritic Cells

The cell wall has a critical role in the host immune response to fungal pathogens. In this study, we investigated the influence of two cell wall fractions of the dimorphic fungi Paracoccidioides brasiliensis (Pb) in the in vitro generation of monocyte-derived dendritic cells (MoDCs). Monocytes were purified from the peripheral blood of healthy donors and cultivated for 7 days in medium supplemented with IL-4 and GM-CSF in the presence of Pb cell wall fractions: the alkali-insoluble F1, constituted by β-1,3- glucans, chitin and proteins, and the alkali-soluble F2, mainly constituted by α-glucan. MoDCs phenotypes were evaluated regarding cell surface expression of CD1a, DCSIGN, HLA-DR, CD80, and CD83 and production of cytokines. The α-glucan-rich cell wall fraction downregulated the differentiation of CD1a+ MoDCs, a dendritic cell subset that stimulate Th1 responses. The presence of both cell fractions inhibited DC-SIGN and HLA-DR expression, while the expression of maturation markers was differentially induced in CD1a- MoDCs. Differentiation upon F1 and F2 stimulation induced mixed profile of inflammatory cytokines. Altogether, these data demonstrate that Pb cell wall fractions differentially induce a dysregulation in DCs differentiation. Moreover, our results suggest that cell wall α-glucan promote the differentiation of CD1a- DCs, potentially favoring Th2 polarization and contributing to pathogen persistence.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Regulation of collagenase-3 and osteocalcin gene expression by collagen and osteopontin in differentiating MC3T3-E1 cells

Both collagenase-3 and osteocalcin mRNAs are expressed maximally during the later stages of osteoblast differentiation. Here, we demonstrate that collagenase-3 mRNA expression in differentiating MC3T3-E1 cells is dependent upon the presence of ascorbic acid, is inhibited in the presence of the collagen synthesis inhibitor, 3,4-dehydroproline, and is stimulated by growth on collagen in the absence of ascorbic acid. Transient transfection studies show that collagenase-3 promoter activity increases during cell differentiation and requires the presence of ascorbic acid. Additionally, we show that, in differentiating MC3T3-E1 cells, collagenase-3 gene expression increases in the presence of an anti-osteopontin monoclonal antibody that binds near the RGD motif of this protein, whereas osteocalcin expression is inhibited. Furthermore, an RGD peptidomimetic compound, designed to block interaction of ligands to the alpha(v) integrin subunit, increases osteocalcin expression and inhibits collagenase-3 expression, suggesting that the RGD peptidomimetic initiates certain alpha(v) integrin signaling in osteoblastic cells. Overall, these studies demonstrate that stimulation of collagenase-3 expression during osteoblast differentiation requires synthesis of a collagenous matrix and that osteopontin and alpha(v) integrins exert divergent regulation of collagenase-3 and osteocalcin expression during osteoblast differentiation.

NASA Discipline Musculoskeletal↗
NASA NTRS1997

Skeletal unloading inhibits the in vitro proliferation and differentiation of rat osteoprogenitor cells

Loss of weight bearing in the growing rat decreases bone formation, osteoblast numbers, and bone maturation in unloaded bones. These responses suggest an impairment of osteoblast proliferation and differentiation. To test this assumption, we assessed the effects of skeletal unloading using an in vitro model of osteoprogenitor cell differentiation. Rats were hindlimb elevated for 0 (control), 2, or 5 days, after which their tibial bone marrow stromal cells (BMSCs) were harvested and cultured. Five days of hindlimb elevation led to significant decreases in proliferation, alkaline phosphatase (AP) enzyme activity, and mineralization of BMSC cultures. Differentiation of BMSCs was analyzed by quantitative competitive polymerase chain reaction of cDNA after 10, 15, 20, and 28 days of culture. cDNA pools were analyzed for the expression of c-fos (an index of proliferation), AP (an index of early osteoblast differentiation), and osteocalcin (a marker of late differentiation). BMSCs from 5-day unloaded rats expressed 50% less c-fos, 61% more AP, and 35% less osteocalcin mRNA compared with controls. These data demonstrate that cultured osteoprogenitor cells retain a memory of their in vivo loading history and indicate that skeletal unloading inhibits proliferation and differentiation of osteoprogenitor cells in vitro.

NASA Discipline Musculoskeletal↗
DOE OSTI2022

Surface analysis insight note: Differentiation methods applicable to noisy data for determination of sp2‐ versus sp3‐hybridization of carbon allotropes and AES signal strengths

The derivatives of the spectra are commonly used for quantification in Auger Electron Spectroscopy (AES) spectra, while the derivative of the KLL C Auger line has proven to be valuable in obtaining a measure of the relative proportions of sp 2 ‐ and sp 3 ‐hybridization using the D‐parameter in both AES and X‐ray Photoelectron Spectroscopy (XPS). Differentiation of X‐ray Photoelectron Spectroscopy (XPS) and Auger Electron Spectroscopy (AES) spectra by numerical means is presented and illustrated for polymeric, such as PEEK and Nylon, as well as for graphitic materials including highly ordered pyrolytic graphite and graphene oxide. The most commonly available Savitzky–Golay method is explained mathematically and developed through the case of constructing a 5‐point quadratic polynomial convolution kernel suitable for differentiating spectra of adequate signal to noise. The concept of differentiation of spectra where signal to noise is less than adequate is also developed. Two alternative strategies to Savitzky–Golay differentiation are presented, which fit curves to data that allow derivatives to be obtained where Savitzky–Golay would otherwise fail. These alternative methods involve constructing a parametric curve that fits data over the entire energy interval of interest. Derivatives of spectra are then obtained by differentiating these parametric curves directly. A comparison of results for different materials for which specific sp 2 ‐ vs sp 3 ‐hybridized carbon proportions are of interest is used to emphasize the importance of characterizing methods used to differentiate spectra and understanding the characteristics of instrumentation used to measure spectra. The case for using Principal Component Analysis noise reduction with C KLL spectra is made for spectra collected from a heterogeneous graphene oxide sample.

Fairley, Neal↗
DOE OSTI2019

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

Purpose: To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard. Methods: We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as “low risk,” a PI-RADSv2 score ≥ 4 as “high risk” for clinically significant PCa. Mean tumor ADC (ADC{sub T}), ADC of adjacent normal tissue (ADC{sub N}), and ADC{sub ratio} (ADC{sub T}/ADC{sub N}) were calculated. Stepwise regression analysis using tumor location, ADC{sub T} and ADC{sub ratio}, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3. Results: 119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADC{sub ratio} was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score (“low” vs. “high”) was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADC{sub T} (p = 0.03) and ADC{sub ratio} (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively. Conclusions: ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

A feasible order-arbitrarily-tunable optical differentiator

We propose a feasible temporal filtering (or spectrum weighting) system to implement optical differentiation with arbitrarily-tunable order. Differentiation order is tuned by programming the temporal weighting mask (TWM) as power function of different exponents, not limited by the structure of the system. Assembly of the system is feasible, since the active parts are implemented by electro-optic elements and some key elements are reused between sub-systems involved in the scheme. Tuning of the system is simple, since electronically pre-stored TWMs are easy to switch among each other. The order-tunable functionality is verified by numerically testing the differentiation cases of normalized input pulse duration ranging from 0.1 to 0.4, and of differentiation order tuned from 1 to 10. Good accuracy is achieved, and measured by root mean square error lower than 0.1. Feasibilities of the system are also verified in terms of accuracy maintaining and distortion avoiding. Compared with existing methods, apart from the arbitrary order-tunable capacity and good accuracy, our proposed optical differentiator also possesses properties of good compactness and constant complexity. The proposed differentiator has unique applications on multi-peak waveform generation for optical communications and photonics-aided Radars with enhanced agility and feasibility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Extracellular IL-37 promotes osteogenic and odontogenic differentiation of human dental pulp stem cells via autophagy

The osteogenic and odontogenic differentiation of dental pulp stem cells (DPSCs) contribute to restoration and regeneration of dental tissue. Previous study indicated that interleukin-37 (IL-37) was an anti-inflammatory factor that affected other pro-inflammatory signals. The aim of this study was to explore the effects of IL-37 on the differentiation of DPSCs. DPSCs were cultured in growth medium with different concentrations of IL-37. We selected the optimal concentration for the following experiments by alkaline phosphatase (ALP) activity analysis, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blot. Cell counting kit assay (CCK-8) and 5-Ethynyl-2-Deoxyuridine (EdU) assay were conducted to assess the effects of IL-37 on the proliferation of DPSCs. ALP activity assay and staining, alizarin red S (ARS) staining, qRT‐PCR, Western blot as well as immunofluorescence staining were conducted to assess differentiation ability of DPSCs. Western blot, immunofluorescence staining and transmission electron microscopy (TEM) were utilized to examine cell autophagy. Results showed that IL-37 enhanced the osteogenic and odontogenic differentiation ability of DPSCs with no significant influence on the proliferation of DPSCs. Autophagy in DPSCs was activated by IL-37. Activation of autophagy enhanced osteogenesis and odontogenesis of DPSCs, whereas inhibition of autophagy suppressed DPSCs osteogenic and odontogenic differentiation. In conclusion, IL-37 increased osteogenic and odontogenic differentiation via autophagy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Paleomagnetic Evidence for a Partially Differentiated Ordinary Chondrite Parent Asteroid

Abstract The textures and accretion ages of chondrites have been used to argue that their parent asteroids never differentiated. Without a core, undifferentiated planetesimals could not have generated magnetic fields through dynamo activity, so chondrites are not expected to have experienced such fields. However, the magnetic remanence carried by the CV chondrites is consistent with dynamo‐generated fields, hinting that partially differentiated asteroids consisting of an unmelted crust atop a differentiated interior may exist. Here, we test this hypothesis by applying synchrotron X‐ray microscopy to metallic veins in the slowly cooled H6 chondrite Portales Valley. The magnetic remanence carried by nanostructures in these veins indicates that this meteorite recorded a magnetic field over a period of tens to hundreds of years at ∼100 Myr after solar system formation. These properties are inconsistent with external field sources such as the nebula, solar wind, or impacts, but are consistent with dynamo‐generated fields, indicating that the H chondrite parent body contained an advecting metallic core and was therefore partially differentiated. We calculate the thermal evolution of the chondritic portions of partially differentiated asteroids that form through incremental accretion across 10 5 to 10 6 years, finding this can agree with the measured ages and cooling rates of multiple H chondrites. We also predict that the cores of these bodies could have been partially liquid and feasibly generating a dynamo at 100 Myr after solar system formation. These observations contribute to a growing body of evidence supporting a spectrum of internal differentiation within some asteroids with primitive surfaces.

Bryson, J. F. J.↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗
DOE OSTI2026

Study of fully coupled three-dimensional envelope instability using automatic differentiation

Automatic differentiation is a powerful tool for computing derivatives of simulation results with respect to given parameters. In this Letter, we have applied this tool to investigate the instability of a dynamical system governed by 21 ordinary differential equations. This second-order instability (named envelope instability) is driven by space-charge effects and has a significant impact on the operational regimes of particle accelerators. Our study delves into the three-dimensional envelope instability, incorporating both transverse and longitudinal coupling. Conventionally, analyzing this complex system would necessitate solving 441 ordinary differential equations, which is computationally intractable. However, by employing automatic differentiation, we were able to track only 21 equations. This approach allowed us to uncover an additional instability stopband, which arises from space-charge-induced coupling and has not been reported in previous studies. This research highlights the significant advantages of automatic differentiation in analyzing complicated dynamical systems involving a large number of ordinary differential equations.

Qiang, Ji [Lawrence Berkeley National Laboratory (↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗