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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systematic Matter and Binding-Energy Distributions from a Dispersive Optical Model Analysis

We present the first systematic dispersive-optical-model analysis of 16,18 O, 40,48 Ca, 58,64 Ni, 112,124 Sn, and 208 Pb. In all systems, roughly half the total nuclear binding energy is associated with the most-bound 10% of the total nucleon density. The extracted neutron skins reveal interplay of asymmetry, Coulomb, and shell effects on the skin thickness. Here, our results indicate that optical-model fits on isotopic pairs are effective for constraining nuclear structural quantities otherwise difficult to observe experimentally.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Systematic study of the validity of the eikonal model including uncertainties

Nuclear reactions at intermediate beam energies are often interpreted using the eikonal model. In the analysis of complex reaction probes, where few-body reaction methods are needed, the eikonal method may be used as an efficient way for describing the fragment-target reaction process. In this work, we perform a systematic study to test the validity of the eikonal approximation for nucleon-nucleus reactions. We also quantify uncertainties due to the nucleon optical potential on reaction observables. We inspect the validity of the eikonal model and its semiclassical correction by comparing it to exact solutions (obtained from solving the optical-model equation with a finite-differences method) for a wide range of reactions. We also study the effect of relativistic corrections, both kinematic and dynamic, by effectively incorporating the relativistic effects at intermediate energies. The uncertainties from a Bayesian global optical potential (KDUQ) are propagated to the observables of interest. Our study includes neutron and proton reactions on 27 Al , 40 Ca , 90 Zr , and 208 Pb , for a wide range of energies 𝐸 lab = 0–400 MeV. We calculate neutron-total cross sections (elastic and reactions) as well as proton-absorption cross sections as a function of beam energy, using the eikonal model, the eikonal model with a semiclassical correction, and the exact solution. Here, we also compute angular distributions for the methods above. Our results show that for the proton-absorption cross section, the eikonal model can be used down to around 60 MeV and the semiclassical correction extends its use to 30 MeV. However, the validity of the eikonal model for the neutron-total cross section only goes down to ≈120 MeV, a range extended to ≈ 50 MeV when using the semiclassical correction. We find the semiclassical correction to the eikonal model to be less effective in describing the angular distributions. The 1⁢𝜎 uncertainty intervals on the observables we studied is less than 5% for most of the energies considered, but increases rapidly for higher energies, namely energies outside the range of KDUQ (𝐸 lab > 200MeV).

Cluster models↗
DOE OSTI2025

Systematic Bayesian evaluation of resonance parameters in 19 Ne for the 15 O ⁡(𝛼, 𝛾)⁢ 19 Ne and 18 F ⁡(𝑝, 𝛼)⁢ 15 O reactions

Here, we present a comprehensive evaluation of the nuclear structure properties of 19 Ne using a novel and rigorous Bayesian statistical framework. Precise characterization of 19 Ne resonance parameters is critical for accurately determining reaction rates of the astrophysically significant 15 O ⁡(𝛼, 𝛾)⁢ 19 Ne and 18 F ⁡(𝑝, 𝛼)⁢ 15 O reactions, which govern breakout from the hot CNO cycle in x-ray bursts and influence 𝛾-ray emission in novae, respectively. By reconstructing likelihood functions from published experimental data—including asymmetric uncertainties and upper or lower limits—we derive posterior distributions for resonance energies, decay widths, and branching ratios. Our Bayesian approach systematically incorporates previously reported discrepancies among measurements, providing a statistically robust and consistent treatment of these uncertainties. The evaluated resonance parameters and associated uncertainties provide crucial input for stellar nucleosynthesis modeling, contributing to a refined understanding of explosive astrophysical phenomena.

Kim, Sohyun H. [Sungkyunkwan Univ., Suwon (Republi↗
DOE OSTI2023

The good, the bad, and the future: Systematic review identifies best use of biomass to meet air quality and climate policies in California

Abstract California has large and diverse biomass resources and provides a pertinent example of how biomass use is changing and needs to change, in the face of climate mitigation policies. As in other areas of the world, California needs to optimize its use of biomass and waste to meet environmental and socioeconomic objectives. We used a systematic review to assess biomass use pathways in California and the associated impacts on climate and air quality. Biomass uses included the production of renewable fuels, electricity, biochar, compost, and other marketable products. For those biomass use pathways recently developed, information is available on the effects—usually beneficial—on greenhouse gas (GHG) emissions, and there is some, but less, published information on the effects on criteria pollutants. Our review identifies 34 biomass use pathways with beneficial impacts on either GHG or pollutant emissions, or both—the “good.” These included combustion of forest biomass for power and conversion of livestock‐associated biomass to biogas by anaerobic digestion. The review identified 13 biomass use pathways with adverse impacts on GHG emissions, criteria pollutant emissions, or both—the “bad.” Wildfires are an example of one out of eight pathways which were found to be bad for both climate and air quality, while only two biomass use pathways reduced GHG emissions relative to an identified counterfactual but had adverse air quality impacts. Issues of high interest for the “future” included land management to reduce fire risk, future policies for the dairy industries, and full life‐cycle analysis of biomass production and use.

Agriculture↗
DOE OSTI2023

A Systematic Investigation of the Effects of Standard-Sample Concentration Mismatch during Fe Isotope Measurement by MC-ICP-MS

Advances in multi-collector inductively coupled plasma-mass spectrometry (MC-ICP-MS) have led to the widespread use of iron (Fe) isotopes to elucidate the (bio)geochemical history of a range of environments. To generate Fe isotope ratio measurements, standard-sample bracketing (SSB) is commonly used to correct for instrumental mass bias inherent to MC-ICP-MS. However, SSB is only accurate when sample and isotope standard Fe concentrations match, in addition to the bulk solution matrix. When the Fe concentrations differ, Fe isotope ratio measurement results may be inaccurate, a phenomenon known as the "self-induced matrix effect." Here, this study systematically characterised the self-induced matrix effect for dry plasma Fe isotope ratio measurements on three MC-ICP-MS instruments and three introduction systems. Our extensive dataset indicates that: (1) the degree of mass bias is consistent regardless of MC-ICP-MS front-end design, (2) the degree of mass bias becomes less reproducible as the concentration difference between the sample and bracketing standard increases, and (3) this applies to both pure Fe solutions and solutions from geological materials. This study reinforces the requirement to match bracketing standard and sample concentrations within 10% and provides a correction method for that fall beyond the recommended concentration range to subsequently allow for proper concentration matching during SSB.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effectiveness of fat grafting in scar reduction: A systematic review and meta‐analysis

Abstract Background Scar brings great physical and mental pain to patients. Fat grafting may be a new and simple way to treat scar. This review aimed to systematically evaluate the correlation between fat grafting and scar reduction. Methods The author searched Chinese and English literature databases such as CNKI, Wanfang Data, Cqvip, Pubmed, Web of Science, Embase, CBM, etc. for articles published in the period from 2011 to 2021 about the randomized controlled trials (RCTs) to assess the effect of fat grating on scar reduction, and then performed meta‐analysis with Review Manager 5.4. Results A total of nine articles were included in the assessment of the effect of fat grafting on scar reduction, with meta‐analysis suggesting statistical significance (OR = 5.63, 95% CI: 3.01–10.53, p < 0.01). There was statistical significance in the difference observed between the simple fat grating group ( p = 0.004) and the group of existing therapies combined with fat grafting ( p < 0.0001). Conclusion Fat grafting may be a good method for scar treatment, especially when it's combined with other therapies. Still, due to the limitations of included RCTs, more large‐scale high‐quality RCTs must be carried out before solid conclusions can be drawn.

Zhang, Xiaoyuan↗
DOE OSTI2025

Secondary corundum‐bearing assemblages in Allende Ca,Al‐rich inclusions: Mineralogy, petrology, oxygen, and aluminum–magnesium isotope systematics

Here, we report on the mineralogy, petrology, oxygen, and aluminum–magnesium isotopic systematics of the secondary corundum-bearing assemblages in type B CAIs 3529Z and 3529G and fluffy type A (FTA) CAI ALH-2 from Allende (CV > 3.6). In 3529Z and 3529G, 2–5 μm-sized euhedral-to-subhedral corundum grains associate with secondary alumoåkermanite [(Ca,Na) 2 AlSi 2 O 7 ], grossular, spinel, grossite, celsian, kushiroite, and wadalite. In ALH-2, 2–5 μm-sized euhedral-to-subhedral corundum grains associate with secondary grossular, nepheline, spinel, and kushiroite. In 3529Z and 3529G, corundum and associated secondary grossite, spinel, alumoåkermanite, grossular, and kushiroite have similar 16 O-poor compositions (Δ 17 O = −2.2 ± 1.5‰); primary spinel is 16 O-rich (Δ 17 O ~ −23‰); Al,Ti-diopside shows a range of Δ 17 O (from ~ −24‰ to ~ −15‰); anorthite and melilite are 16 O-depleted to various degrees (−6.5‰ ≤ Δ 17 O ≤ −4.5‰ and Δ 17 O = −2.7 ± 0.8‰, respectively). In ALH-2, corundum shows a range of Δ 17 O, from ~ −9‰ to ~ −1‰; primary hibonite and spinel are 16 O-rich (Δ 17 O ~ −23‰); melilite and perovskite are 16 O-poor (Δ 17 O = −2.6 ± 1.5‰ and −3.1 ± 1.3‰, respectively). On the Al-Mg isotope diagram ( 26 Mg* versus 27 Al/ 24 Mg), primary Al,Ti-diopside, hibonite, melilite, and spinel in the Allende CAIs studied along the canonical isochron with inferred initial 26 Al/ 27 Al ratio [( 26 Al/ 27 Al) 0 ] of ~5 × 10 −5 . All secondary minerals have resolved excesses of 26 Mg*: alumoåkermanite, corundum, and grossite plot below the canonical isochron, whereas most spinel analyses plot above it. An internal isochron defined by the coexisting secondary corundum and alumoåkermanite in 3529Z has ( 26 Al/ 27 Al) 0 = (7.5 ± 2.6) × 10 −7 . We conclude that the corundum-bearing assemblages in Allende CAIs resulted from metasomatic alteration of primary melilite and anorthite, ~4–5 Ma after their crystallization. Metasomatic alteration of CAIs in the Allende parent asteroid by an aqueous fluid having Δ 17 O of ~ −3 ± 2‰ modified the O-isotope composition of their primary melilite, anorthite, and Ti-rich pyroxene; O-isotope compositions of primary hibonite, spinel, and low-Ti pyroxene escaped this modification.

Geosciences↗
DOE OSTI2024

Enhancing risk and crisis communication with computational methods: A systematic literature review

Abstract Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

Mathematical Methods In Social Sciences↗
DOE OSTI2021

Serial femtosecond and serial synchrotron crystallography can yield data of equivalent quality: A systematic comparison

For the two proteins myoglobin and fluoroacetate dehalogenase, we present a systematic comparison of crystallographic diffraction data collected by serial femtosecond (SFX) and serial synchrotron crystallography (SSX). To maximize comparability, we used the same batch of micron-sized crystals, the same sample delivery device, and the same data analysis software. Overall figures of merit indicate that the data of both radiation sources are of equivalent quality. For both proteins, reasonable data statistics can be obtained with approximately 5000 room-temperature diffraction images irrespective of the radiation source. The direct comparability of SSX and SFX data indicates that the quality of diffraction data obtained from these samples is linked to the properties of the crystals rather than to the radiation source. Therefore, for other systems with similar properties, time-resolved experiments can be conducted at the radiation source that best matches the desired time resolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring

A combined machine learning–physics–based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system’s free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

42 ENGINEERING↗
DOE OSTI2025

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the τ τ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Systematics of the chemical freeze-out line in the high baryon density regime explored at SIS100

The systematic uncertainties of chemical freeze-out fits at SIS100 energies (Au+Au reactions at $\sqrt{s_{NN}}$ = 3 – 5 GeV) are studied using UrQMD simulations. Although hadron production in UrQMD does not occur on a sharp chemical freeze-out hyper-surface, the extracted fit quality is shown to be very good. The extracted chemical parameters depend on the selected hadron species as well as the underlying equation of state (EoS) of the matter. Including light nuclei and anti-protons in the fit increases the expected freeze-out temperature, while a stiffer EoS increases the obtained chemical potential. Similarly, the baryon densities extracted by the thermal fits depend on the choice of hadrons as well as the underlying equation of state. These results are important for the upcoming CBM@FAIR physics program and highlight that a degree of caution is advised when one relates the chemical freeze-out curve to features on the QCD phase diagram like the critical endpoint or a possible phase transition.

Hofmann, Emma Lilith [GSI Helmholtzzentrum für Sch↗
DOE OSTI2020

Systematic investigation of the particle spectra in heavy-ion collisions at the Large Hadron Collider

We investigate the charged particle spectra produced in the heavy-ion collisions at nine centralities from different systems, i.e. Pb+Pb at √ s NN = 2.76 TeV and 5.02 TeV as well as Xe+Xe at √ s NN = 5.44 TeV, at Large Hadron Collider (LHC) using one empirical formula inspired by the stationary solution of the Fokker-Planck equation, dubbed as the generalized Fokker-Planck solution (GFPS). Our results show that the GFPS can reproduce the experimental particle spectrum up to transverse momentum pT about 45 GeV/c with the maximum discrepancy 30% covering 10 orders of magnitude. Here, the discrepancy between the data and the results from the GFPS decreases to 15% when the maximum of the charged particle transverse momentum is cut to 20 GeV/c. We confirmed that the Tsallis distribution derived from the non-extensive statistics, which can reproduce the particle spectra produced in small collision systems, such as p+p, up to few hundreds GeV/c, can only apply to systematically study the particle spectra up to 8 GeV/c in A+A collisions at LHC, as pointed out in the study of identified particle spectra in Pb+Pb collisions at √ s NN = 2.76 TeV. A brief discussion on GFPS is also given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

QUEST: systematically approximating Quantum circuits for higher output fidelity

We present QUEST, a procedure to systematically generate approximations for quantum circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for scalability with procedures to 1) reduce circuit length using approximate synthesis, 2) improve fidelity by running circuits that represent key samples in the approximation space, and 3) reason about approximation upper bound. Our evaluation results indicate that our approach of "dissimilar"approximations provides close fidelity to the original circuit. Overall, the results indicate that QUEST can reduce CNOT gate count by 30-80% on ideal systems and decrease the impact of noise on existing and near-future quantum systems.

Patel, Tirthak↗
DOE OSTI2023

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

Research & Experimental Medicine↗
DOE OSTI2025

A systematic decision-making methodology to formalize the selection of degree of realism in screening analysis of probabilistic risk assessment

In the nuclear power domain, Probabilistic Risk Assessment (PRA) is used to inform decision-making for Nuclear Power Plants (NPPs). Recently, there has been an increase in the utilization of modeling and simulation (M&S) to support the estimation of PRA inputs. Risk analysts should carefully select the PRA items that require M&S and their degree of realism (DoR) with consideration of the required resources. To support this selection, this article formulates a systematic decision-making approach for the DoR selection. The DoR selection is made based on two predictive decision-making attributes: the predicted differences in safety risk estimate (ΔSaRi) and the cost of analysis (ΔCAN). This research also develops and quantifies causal models to estimate ΔSaRi and ΔCAN. The causal model-based prediction of ΔSaRi and ΔCAN helps reduce the trial-and-error nature of the DoR selection in the PRA screening analysis and provides insights for DoR selection and the gradual refinements of PRA realism. This approach is demonstrated for a case study on fire PRA of NPPs, where an adequate DoR is selected from two fire models: an engineering correlation and a zone model.

Alkhatib, Sari [Department of Nuclear, Plasma, and↗
DOE OSTI2023

Systematic approaches to C-lignin engineering in Medicago truncatula

Abstract Background C-lignin is a homopolymer of caffeyl alcohol present in the seed coats of a variety of plant species including vanilla orchid, various cacti, and the ornamental plant Cleome hassleriana . Because of its unique chemical and physical properties, there is considerable interest in engineering C-lignin into the cell walls of bioenergy crops as a high-value co-product of bioprocessing. We have used information from a transcriptomic analysis of developing C. hassleriana seed coats to suggest strategies for engineering C-lignin in a heterologous system, using hairy roots of the model legume Medicago truncatula . Results We systematically tested strategies for C-lignin engineering using a combination of gene overexpression and RNAi-mediated knockdown in the caffeic acid/5-hydroxy coniferaldehyde 3/5-O-methyltransferase ( comt ) mutant background, monitoring the outcomes by analysis of lignin composition and profiling of monolignol pathway metabolites. In all cases, C-lignin accumulation required strong down-regulation of caffeoyl CoA 3- O -methyltransferase (CCoAOMT) paired with loss of function of COMT. Overexpression of the Selaginella moellendorffii ferulate 5-hydroxylase ( SmF5H ) gene in comt mutant hairy roots resulted in lines that unexpectedly accumulated high levels of S-lignin. Conclusion C-Lignin accumulation of up to 15% of total lignin in lines with the greatest reduction in CCoAOMT expression required the strong down-regulation of both COMT and CCoAOMT, but did not require expression of a heterologous laccase, cinnamyl alcohol dehydrogenase (CAD) or cinnamoyl CoA reductase (CCR) with preference for 3,4-dihydroxy-substituted substrates in M. truncatula hairy roots. Cell wall fractionation studies suggested that the engineered C-units are not present in a heteropolymer with the bulk of the G-lignin.

09 BIOMASS FUELS↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗