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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods’ ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

An Update to the Letter of Intent for MATHUSLA: Search for Long-Lived Particles at the HL-LHC

We report on recent progress in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC, updating the information in the original Letter of Intent (LoI), see CDS:LHCC-I-031, arXiv:1811.00927. A suitable site has been identified at LHC Point 5 that is closer to the CMS Interaction Point (IP) than assumed in the LoI. The decay volume has been increased from 20 m to 25 m in height. Engineering studies have been made in order to locate much of the decay volume below ground, bringing the detector even closer to the IP. With these changes, a 100 m x 100 m detector has the same physics reach for large c$\tau$ as the 200 m x 200 m detector described in the LoI and other studies. The performance for small c$\tau$ is improved because of the proximity to the IP. Detector technology has also evolved while retaining the strip-like sensor geometry in Resistive Plate Chambers (RPC) described in the LoI. The present design uses extruded scintillator bars read out using wavelength shifting fibers and silicon photomultipliers (SiPM). Operations will be simpler and more robust with much lower operating voltages and without the use of greenhouse gases. Manufacturing is straightforward and should result in cost savings. Understanding of backgrounds has also significantly advanced, thanks to new simulation studies and measurements taken at the MATHUSLA test stand operating above ATLAS in 2018. We discuss next steps for the MATHUSLA collaboration, and identify areas where new members can make particularly important contributions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Experimental study of multiple-orientation muon tomography with image optimization in sparse data environments

Due to the high penetrating power of cosmic-ray muons, they can be used to probe very thick and dense objects. As muons are charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object to measure particle direction change, scattering information within the object can be found. This can be used to produce a scattering-intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies were published with multiorientation tomography, which can form a three-dimensional representation faster than a single-orientation view. In this study, experimental muon-scatter-based tomography was performed using a concrete filled steel drum with several different metal wedges inside, with the drum between detector planes. Data were collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were combined using two different tomographic methods. A traditional inverse Radon transform approach used for computed tomography and a combination of multiple depth-of-field reconstructions were applied to the data. As cosmic-ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedges.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Studying AC-LGAD strip sensors from laser and testbeam measurements

Here, this paper presents the setup assembled to characterize and measure the spatial and timing resolutions of AC-coupled Low Gain Avalanche Diodes (AC-LGADs), using a 1060 nm laser source to deposit initial charges with a defined calibration methodology. The results were compared to those obtained with a 120 GeV proton beam. Despite the differences in the charge deposition mechanism between the laser and proton beam, the spatial and temporal resolutions were found to be compatible between the two sources after calibration. With 4D tracking detectors expected to play a vital role in upcoming collider experiments, we foresee this work as a way to evaluate the performance of semiconductor sensors that can augment testbeam measurements and accelerate R&D efforts. Additionally, simulation studies using Silvaco TCAD and Weightfield2 were carried out to understand the various contributing factors to the total time resolution in AC-LGAD sensors, measured using the laser source.

FOS: Physical sciences↗

DOE OSTI2024

Chemical bond reorganization in intramolecular proton transfer revealed by ultrafast X-ray photoelectron spectroscopy

Time-resolved X-ray photoelectron spectroscopy (TR-XPS) is used in a simulation study to monitor the excited state intramolecular proton transfer between oxygen and nitrogen atoms in 2-(iminomethyl)phenol. Real-time monitoring of the chemical bond breaking and forming processes is obtained through the time evolution of excited-state chemical shifts. By employing individual atomic probes of the proton donor and acceptor atoms, we predict distinct signals with opposite chemical shifts of the donor and acceptor groups during proton transfer. Details of the ultrafast bond breaking and forming dynamics are revealed by extending the classical electron spectroscopy chemical analysis to real time. Through a comparison with simulated time-resolved photoelectron spectroscopy at the valence level, the distinct advantage of TR-XPS is demonstrated thanks to its atom specificity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Practical considerations for measuring global spin density matrix elements of vector mesons in heavy-ion collisions

The STAR Collaboration has reported a significant 𝜙-meson global spin alignment (𝜌 00 ) signal in Au+Au collisions at $\sqrt{s_{NN}}$ ≤ 62 GeV by measuring the polar angle distribution of 𝜙-meson daughters with respect to the orbital angular momentum direction of the collision system. Here, in this paper, a new method is explored for studying vector-meson global spin alignment in heavy-ion collisions by examining the two-dimensional (2D) polar and azimuthal angle distribution. This method allows simultaneous extraction of 𝜌 00 and off-diagonal spin density matrix elements (SDMEs), providing unique access to local quark-antiquark spin correlations and spin hydrodynamics in quark-gluon plasma. The new 2D method also removes potential biases from nonzero off-diagonal SDMEs on 𝜌 00 with the 1D method. A detailed procedure to correct for detector acceptance and resolution effects is also presented and validated by simulation studies.

Wilks, Gavin [University of Illinois, Chicago, IL ↗

DOE OSTI2022

Fast and fault-tolerant model predictive control of MMCs under selective harmonic elimination

Selective harmonic elimination (SHE) is a suitable pulse-width modulation (PWM) tech-nique for some applications of modular multilevel converters (MMC) with lower numberof semiconductor submodules (SM). However, SHE can make it difficult to control circu-lating currents in the MMC. Moreover, the MMC is vulnerable to SM switch failures thatreduce its output power magnitude and quality. This paper proposes a fault-tolerant modelpredictive control strategy for MMCs operating with SHE (FT SHE-MPC). The effects ofswitch failure on phase voltage waveforms are studied first. Then, a method is proposedto design the FT SHE waveform to preserve its harmonic performance. Next, the pro-posed FT SHE-MPC is designed to increase the capacitor voltages during an SM failure tomaintain the fundamental component of the faulty phase voltage and control the circulat-ing currents, maintaining balance among line voltages and the output power of the MMC.Additionally, the proposed method offers as much as 60% faster FT transition time and asmuch as 36% lower capacitor voltage ripple. The advantages of the proposed method areverified through offline and real-time simulation studies on a three-phase nine-level MMCin PSCAD/EMTDC software and RTDS.

42 ENGINEERING↗

DOE OSTI2020

Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions

This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks, so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities, but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. Finally, the main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Intrinsic Covariate Information

Not provided.

Automation & Control Systems↗

DOE OSTI2024

Global Distribution of EMIC Waves and Its Association to Subauroral Proton Precipitation During the 27 May 2017 Storm: Modeling and Multipoint Observations

Recent simulation studies using the RAM-SCB model showed that proton precipitation contributes significantly to the total energy flux deposited into the subauroral ionosphere thereby affecting the magnetosphere-ionosphere coupling. Here, in this study, we use the BATS-R-US + RAM-SCB model to understand the evolution of ElectroMagnetic Ion Cyclotron (EMIC) waves in the inner magnetosphere, their correspondence to the proton precipitation into the subauroral ionosphere, and to assess the performance of the model in reproducing the EMIC wave-particle interactions. During the 27 May 2017 storm, Arase and RBSP-A satellites observed typical signatures of EMIC waves in the inner magnetosphere. Within this interval, Defense Meteorological Satellite Program (DMSP) and National Oceanic and Atmospheric Administration (NOAA)/MetOp satellites observed significant proton precipitation in the dusk-midnight sector. Simulation results show that H- and He-band EMIC waves are excited within regions of strong temperature anisotropy near the plasmapause. The simulated growth rates of EMIC waves show a similar trend to that of the EMIC wave power observed by the Arase and RBSP-A satellites, suggesting that the model can reproduce the EMIC wave activity qualitatively. The simulated H-band waves in the dusk sector are stronger than He-band waves possibly due to the presence of excess protons in the boundary conditions obtained from the BATS-R-US code. The precipitating proton fluxes reproduced by the simulation with EMIC waves are found to agree reasonably well with the DMSP and NOAA/MetOp satellite observations. It is suggested that EMIC wave scattering of ring current ions can account for proton precipitation observed by the DMSP and MetOp satellites during the 27 May 2017 storm.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

New opportunities for rare charm from $Z\rightarrow c\bar{c}$ decays

We analyze the potential of rare charm decays as probes of new physics at a high-luminosity flavor facility operating at the Z pole, such as the FCC-ee or CEPC. In particular, we identify clean null-test observables in $D^0 → π^+ π^- v\bar{v}$ and in polarized $Λ^+_c → p ℓ^+ ℓ^-$ decays with $ℓ = e, μ$. Complementarity with the LHC and HL-LHC flavor programs arises from the characteristic features of a Tera-Z environment: the capability to study missing-energy modes and charm production with significant polarization. We improve the theoretical description of $D^0 → π^+ π^- v\bar{v}$ decays and work out the phenomenology of polarization-induced null-test observables in $Λ^+_c → p ℓ^+ ℓ^-$ decays. In regions of dilepton mass near the φ resonance, polarization asymmetries can reach $O$(5%) for muons and $O$(14%) for electrons times the $Λ^+_c$ polarization. We also point out synergies between the dineutrino and the dilepton modes using the SMEFT framework of heavy new physics. Using the IDEA detector concept at FCC-ee, we find in simulation studies that dineutrino branching fractions as low as ~ 2 x 10 -7 can be probed, which reaches well into the parameter space of new physics, and also allows for discrimination of lepton flavor structures. Furthermore, the measurement of asymmetries in $Λ^+_c → p ℓ^+ ℓ^-$ at $O$(1%) will be possible. Similar sensitivities are expected for dielectron final states, although robust predictions will require further dedicated studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Comparative Study of Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. Here, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗

DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

100% microgrid↗

DOE OSTI2024

Turning Normal to Abnormal: Reversing CO 2 /C2–Hydrocarbon Selectivity in HKUST–1

Metal–organic frameworks (MOFs) can efficiently purify hydrocarbons from CO 2 , but their rapid saturation, driven by preferential hydrocarbon adsorption, requires energy-intensive adsorption–desorption processes. To address these challenges, an innovative approach is developed, enabling control over MOF flexibility through densification and defect engineering, resulting in an intriguing inverse CO 2 /C2 hydrocarbon selectivity. Here, in this study, the densification process induces the shearing of the crystal lattice and contraction of pores in a defective CuBTC MOF. These changes have led to a remarkable transformation in selectivity, where the originally hydrocarbon-selective CuBTC MOF becomes CO 2 -selective. The selectivity values for densified CuBTC are significantly reversed when compared to its powder form, with notable improvements observed in CO 2 /C 2 H 6 (4416 vs 0.61), CO 2 /C 2 H 4 (15 vs 0.28), and CO 2 /C 2 H 2 (4 vs 0.2). The densified material shows impressive separation, regeneration, and recyclability during dynamic breakthrough experiments with complex quinary gas mixtures. Simulation studies indicate faster CO 2 passage through the tetragonal structure of densified CuBTC compared to C 2 H 2 . Experimental kinetic diffusion studies confirm accelerated CO 2 diffusion over hydrocarbons in the densified MOF, attributed to its small pore window and minimal interparticle voids. This research introduces a promising strategy for refining existing and future MOF materials, enhancing their separation performance.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Grain boundary relaxation in doped nano-grained aluminum

Here, simulation studies are done to understand the role of dopants that segregate preferentially to grain boundaries on the stability of nanocrystalline aluminum. A dopant design framework based on thermodynamic principles, is used to identify the specific dopant type with the highest potential to segregate to grain boundaries in nanocrystalline aluminum. Various elements are evaluated as potential dopants and magnesium is identified to have the highest tendency to segregate to grain boundaries and release the excess free energyleading to the relaxation of the grain boundaries. A systematic combination of atomic structure analysis is then done to correlate grain boundary relaxation and the mechanical response of the magnesium-doped nanocrystalline aluminum at ambient temperature. The atomistic simulations reveal that the preferential partitioning of magnesium dopants to the grain boundaries reduces the excess volume within this region which stabilizes the nanostructure. At low contents, the magnesium dopants are observed initially partition to the grain boundaries, but once saturation of the grain boundaries is reached, excess dopants are accommodated in the crystalline interiors. It is found that the addition of the magnesium dopants even in the dilute limit, enhances the strength of the nanocrystalline aluminum. The formation of large, disordered GBs in doped nanocrystalline aluminum under tensile load allowed it to accommodate the deformation and prohibit crack growth.

36 MATERIALS SCIENCE↗