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Measurements of the production cross-section for a Z boson in association with b - or c -jets in proton–proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

This paper presents a measurement of the production cross-section of a Z boson in association with b- or c-jets, in proton–proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS experiment at the Large Hadron Collider using data corresponding to an integrated luminosity of 140 fb –1 . Inclusive and differential cross-sections are measured for events containing a Z boson decaying into electrons or muons and produced in association with at least one b-jet, at least one c-jet, or at least two b-jets with transverse momentum p T > 20 GeV and rapidity |y| < 2.5. Predictions from several Monte Carlo generators based on next-to-leading-order matrix elements interfaced with a parton-shower simulation, with different choices of flavour schemes for initial-state partons, are compared with the measured cross-sections. The results are also compared with novel predictions, based on infrared and collinear safe jet flavour dressing algorithms. Selected Z+≥ 1 c-jet observables, optimized for sensitivity to intrinsic-charm, are compared with benchmark models with different intrinsic-charm fractions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Fracture Sustainability Test, Pre- and Post-Test Photomicrographs

The primary objective of this research is to understand how different rock types, mineral and fluid compositions, and fracture surface textures determine the longevity of fracture apertures, so that selection of reservoir rock can be economically optimized to reduce future refracturing. We are performing laboratory tests to study this in a custom apparatus at conditions relevant to EGS, with temperatures up to 250 degrees C (design maximum 300 degrees C). Our approach is to perform a number of long term (up to several months) laboratory experiments using relevant rock samples with different mineralogies to explore fracture sustainability under EGS conditions. We use an apparatus that allows direct application of a normal force on the fracture faces of a single fracture in a sample having a sheared, tensile fracture. We flow brine of a specified composition through the aperture, and simultaneously measure the fracture permeability and closure. We collect the effluent water for chemical and isotopic analysis. We are numerically modeling our tests and comparing experimental and numerical results. This submission includes photomicrographs of pre-test (unreacted) and post-test (reacted) samples from Brady well BCH-03 at various depths, Desert Peak well DP 35-13, and samples of Stripa granite. The photomicrographs are provided using uncrossed and crossed polarized light (xpl). UN is uncrossed nicols, CN and xpl are crossed nicols (crossed polars). The magnification listed is just referring to the objective lens that was used, not the total magnification of the images. With a 5x objective, the bottom dimension of an image is 1.75 mm. With 10x the bottom dimension of an image is 0.875 mm, and with 2x the bottom dimension of an image is 4.375 mm.

15 GEOTHERMAL ENERGY↗

Final Report for "Non-Accelerator Physics – Research in High Energy Physics: Dark Energy Research on DES"

One of the greatest mysteries in modern cosmology is the fact that the expansion of the universe is observed to be accelerating. This acceleration may stem from dark energy, an additional energy component of the universe, or may indicate that the theory of general relativity is incomplete on cosmological scales. The growth rate of large-scale structure in the universe and particularly the largest collapsed structures, clusters of galaxies, is highly sensitive to the underlying cosmology. Clusters will provide one of the single most precise methods of constraining dark energy with the ongoing Dark Energy Survey (DES). The accuracy of the cosmological constraints derived from DES clusters necessarily depends on having an optimized and well-calibrated algorithm for selecting clusters as well as an optical richness estimator whose mean relation and scatter compared to cluster mass are precisely known. Calibrating the galaxy cluster richness-mass relation and its scatter was the focus of the funded work. Specifically, we employ X-ray observations and optical spectroscopy with the Keck telescopes of optically-selected clusters to calibrate the relationship between optical richness (the number of galaxies in a cluster) and underlying mass. This work also probes aspects of cluster selection like the accuracy of cluster centering which are critical to weak lensing cluster studies.

43 PARTICLE ACCELERATORS↗

Validation Data Acquisition in HTTF during PCC Events

A validation experimental campaign was conducted in an Integral Effect Test (IET) facility of High Temperature Gas Reactors (HTGR), the High-Temperature Test Facility (HTTF) at Oregon State University (OSU). The HTTF simulates Depressurized and Pressurized Conduction Cooldowns (DCC and PCC). This campaign required the development of a new laser spectroscopic diagnostic to measure velocity in the challenging conditions of the HTTF: low speed (~1 m/s) gas flows at HTGR prototypical temperature and 1/10th pressure. This was a collaborative effort between co-PIs at The George Washington University (GW), Oregon State University (OSU), and NASA Langley Research Center. The main accomplishments of this project include the record for dynamic range for velocimetry, highest accuracy obtained with this technique, successful deployment in an IET leading to new validation matrix for CFD. These are detailed below and in manuscript appended to this executive summary. For this project, we introduced a new class of laser spectroscopic diagnostics to Thermal-Hydraulics to measure velocity of gases; furthermore, the diagnostic was demonstrated in-situ in an IET during DCC events. In such scenarios, particles used in mainstream techniques, like Particle Image Velocimetry (PIV) are not appropriate as they settle down too rapidly and also contaminate the experimental facility. Molecular tracers stay mixed within the working gas and can seed the flow in a technique called Molecular Tagging Velocimetry (MTV). In MTV a molecular tracer is photo-dissociated by a first (write) laser into radicals or molecules. The pattern created by the write laser is then interrogated with planar laser-induced fluorescence (PLIF), the read pulse(s), which are recorded with a camera. The pattern is probed and matched at two times (interval or probe time, dt), resulting in a time-of-flight velocimetry technique. This project demonstrated a new application range for MTV in gases. MTV has been extensively used for high-speed gas dynamics: molecules do not suffer from inertia associated with particles in regions of high accelerations (such as shock waves). For high-speed flows, the probe time is on the order of 0.1-10 µs. In time-of-flight techniques, the velocity is reconstructed from the recorded displacement, dx, between time interval dt: U = dx/dt. To first order, the resolution of the pattern matching is typically a (fixed) fraction of a pixel; therefore the resolution of the velocity measurement increases with dx and, for a given flow velocity, dt. For the low-speed flows of interest here, this requires long probe time, and therefore finding tracers that are stable for a long time. After an extensive literature survey we identified 6 tracers that would be suitable. We also worked with Spectra Physics to design a custom laser system that is flexible enough to probe these various tracers. The final instrument is made of 4 lasers that are integrated into a compact and movable cart to facilitate the deployment of the instrument off-site. One unique aspect of the laser system is the tunable dye laser (needed to do PLIF of the radicals). By being pumped by two Nd:YAG lasers, this laser is able to provide two pulses with any probe time (dt) for wavelengths between 200 and 900 nm with a linewidth of 3 pm. Accommodating the dual-pulse capability required customizing the pump lasers and the dye laser, making this system unique. Additionally, a custom UV-intensified CCD camera capable of frame-straddling and exposure time as short as 10 ns was acquired. More than two-third of the instrument cost (~$200k) was covered by a cost sharing from the PI’s startup funds. Of the 6 potential tracers identified, two were tested extensively (H2O and N2O) at high-temperature and elevated pressure at GW prior to conducting the tests at OSU. For both tracers, an Ar-F laser (193 nm, 10 ns, 10 mJ/pulse) photo-dissociates trace amounts (0.1 – 5% molar fraction) of the gases into OH and NO. OH is probed at 281.905 nm and NO at 226.186 nm (4-30mJ, 10 ns pulse). NO is very stable in inert environment and we could reconstruct velocities precisely with probe delay times as long at 40 ms, compared to 2 ms for OH, resulting in increased precision. Therefore, for the tests in the HTTF, N2O was selected as the final tracer and its optimal concentration was determined to be 0.5% molar. In the GW laboratory, we obtained a velocity measurement precision of 4 mm/s at conditions mimicking the HTTF tests. We also demonstrated the benefit of molecular tracers over particles and the flexibility of our instrument by continuously measuring velocity from 1000 to 0.1 m/s in a blow down test. This represents a dynamic range on velocity of 80 dB, which is unprecedented for velocimetry! Once the diagnostic was optimized, two two-week campaigns were conducted in the HTTF. This experimental campaign required numerous practical solutions to successfully and rapidly deploy this very sensitive table-top diagnostic in-situ. This was accomplished thanks to very extensive planning from all parties involved in the project. Due to constraints on availability of the HTTF, only adiabatic DCC tests were conducted. Specifically, the velocity was measured at the exit of the hot leg into the Reactor Cavity Simulation Tank (RCST). We were able to record the gas velocity associated with the air ingress for long times (up to one hour). Depending on the initial conditions (density of the gases in the Reactor Pressure Vessel and the RCST) the lock exchange lasted 30-60 s past the valve openings. Large vortices were identified within the shear layer at the interface of the cross flow. These vortices are an indication of shear-driven instability between the counter streams of different density. After the lock-exchange, a small residual flow was still present and its strength decayed exponentially. It finds its source in a buoyancy difference between the gases in both vessels due to the mixing and diffusion of the gases in the cross-over duct and the RPV lower plenum. We did not expect to capture this part of the transient, which would have been less significant (if not negligible) in the presence of a heated core; the precision of our measurement in the HTTF was 6 mm/s, which exceeded our expectations for such a complex test! Using commercial software (Fluent) we simulated the experiment with Computational Fluid Dynamic (CFD) using Reynolds Averaged Navier-Stokes (RANS) turbulence models. We were not able to accurately reproduce the shear layer instability, mixing, and diffusion captured in our experimental data, even with large 3D meshes (>10 M points). We attributed this to the inadequacy of the RANS models for the flow of interest: our experimental Reynolds number was too low and was outside the applicability range of the RANS models. Instead, we should have employed CFD with less modeling, such as Large Eddy (LES) or even Direct Numerical (DNS) simulations. Unfortunately, we did not have the computational resources to do so. Finally, it should be noted that this experimental technique holds great promises for measuring velocity in thermalhydraulics. MTV has an application domain (highlighted in the narrative) that is complimentary to more established (and simpler) techniques, such as PIV. It has attracted the attention of several thermalhydraulics researchers and sponsors to measure velocity in harsh environments. The spectroscopic nature of this technique also enables to expand it to measure temperature and concentration simultaneously to velocity. In lieu of a narrative, 4 detailed journal publications are appended to this executive summary to describe the various development and accomplishments during this project. They include 2 published manuscripts (Experiments in Fluids and Measurement Science and Technology), and two invited contributions following the NURETH-17 conference (Nuclear Science and Technology). Our current Nuclear Engineering and Design draft is not included at this time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Unitary Thermal Energy Management for Propulsion Range Augmentation (UTEMPRA)

Battery Electric Vehicles (BEVs) encounter weather-dependent driving range. A major drain on the vehicle battery is the HVAC system which, when typically using resistive heating only, can result in a reduction of up to 50% of the driving range in winter weather. In this project, a BEV thermal management system is designed and demonstrated on a Fiat 500e BEV. The system comprises a simple, compact, semi-hermetic refrigerant sub-system and an accompanying a coolant circulatory system which, through an innovative valve arrangement, is used to transfer heat between the several heat sources and sinks in the BEV. The system is capable of A/C and heat pump operation, thermal management of the traction battery and powertrain electronics and also recycles waste heat of the vehicle powertrain to heat the cabin to further converse energy. An automated control system is implemented and tested that selects the relevant mode of operation and optimizes energy usage while meeting vehicle thermal requirements. In wind tunnel tests, the vehicle and UTEMPRA system demonstrated 13% (the target of the project was set at 15%) range increase at -10°C driving at a steady 40 kph speed. The vehicle also demonstrated full capability to automatically control thermal conditions at a wide range of ambient temperatures thus demonstrating the viability of such a system in practice.

33 ADVANCED PROPULSION SYSTEMS↗

Investigation of Ga 2 O 3 as a new transparent conductive oxide for photovoltaics applications

This small innovation project intended to leverage recent activity and advances in ultrawide-bandgap oxide power electronics toward the investigation of Ga 2 O 3 as a transparent conductive oxide (TCO) for use in photovoltaics (PV). Ga 2 O 3 ’s theoretical advantage over incumbent TCOs is its very large bandgap of 4.8 eV, ensuring optical transparency of photons with λ ≥ 260 nm, effectively the full terrestrial solar spectrum. At the time of proposal writing, literature on Ga 2 O 3 as a TCO/optical material was relatively sparse — what little existed was focused on for UV sensors and/or emitters, with none related to PV. As such, this project was intended to help determine the potential of Ga 2 O 3 as a PV-oriented TCO by investigating its deposition using tools common to the PV and electronics industries — atomic layer deposition (ALD) and RF sputtering (RS), both of which are already used in PV manufacturing — and its resultant optical and electronic properties. Final project goals were to test its application to Si and III-V solar cells. Controllable film thickness and excellent uniformity was established for both methods deposition methods, with ALD providing higher precision for thinner films and RS more effective for growing thicker films. The as-deposited films were found to be amorphous in nature. Spectroscopic ellipsometry (SE) confirmed bandgaps of at least 4.8 eV and non-parasitic absorption across both AM0 and AM1.5G spectra; a refractive index approaching the expected value of 1.8 was also observed, with some degree of tunability based on process parameters. Using this initial optical data, a transfer matrix model, which interfaces with in-house EQE and LIV models, was developed to simulate Ga 2 O 3 optical effects on various solar cells, included potential in antireflection coatings (ARCs). Despite promising optical properties, the resultant resistivity / conductivity metrics did not meet expectations, regardless of deposition process. Although thick RS-deposited films, using both undoped and 1 at% Ge (n-type) doped sintered targets — demonstrated high net carrier concentrations (via C-V), and low specific resistance Ohmic contacts were demonstrated, transmission line measurements (TLM) showed very high resistivity. Further analysis indicated high vertical film conductivity, but very low lateral conductivity. Deeper characterization revealed a relatively high density of nano/microcrystalline inclusions that were ostensibly the source of the vertical conductivity, with the amorphous matrix serving as an effective insulator (likely due to high concentrations of electronic trap states). Despite extensive work to increase film polycrystallinity — demonstrated via high temperature annealing — sufficient lateral conductivity for TCO use was not achieved. The final phase of the project shifted focus toward investigation of ALD-deposited Ga 2 O 3 ’s potential as a passivant and/or passivating contact for both Si and III-V solar cells, with expectation of performance similar to Al 2 O 3 . However, initial rounds of testing using our baseline ALD process yielded minimal (but not quite zero) passivation of both GaInP and Si surfaces. Dielectric passivation is well-known to be highly process sensitive; given the unoptimized nature of the process used, this work was deemed inconclusive. The final outlook with respect to feasibility of Ga 2 O 3 as a PV-oriented TCO is, ultimately, still uncertain. The work performed in this project confirmed the optical properties and deposition methods, but the achieved electrical properties do not meet technological needs; literature reports in recent years are somewhat inconsistent and potentially untrustworthy, but generally appear to be in line with our results. At the very least it is clear that, should Ga 2 O 3 still be under consideration for PV TCO and/or selective contact applications, a significant amount of optimization and study is still needed.

14 SOLAR ENERGY↗

Transitioning from Stage-III to Stage-IV: Cosmology from galaxy×CMB lensing and shear×CMB lensing (Final Report for Grant SC0021429 "Cosomology with Galaxy and CMB Surveys: Mitigating Systematic Effects Through Combining Datasets")

We examine the cosmological constraining power from two cross-correlation probes between galaxy and cosmic microwave background (CMB) surveys: the cross-correlation of lens galaxy density with CMB lensing convergence $\langle δ_g$$^κ\text{CMB}\rangle$, and source galaxy weak lensing shear with CMB lensing convergence $\langle^{γκ}$$_{\text{CMB}}\rangle$. These two cross-correlation probes provide an independent cross-check of other large-scale structure constraints and are insensitive to galaxy-only or CMB-only systematic effects. In addition, when combined with other large-scale structure probes, the cross-correlations can break degeneracies in cosmological and nuisance parameters, improving both the precision and robustness of the analysis. In this work, we study how the constraining power of $\langle δ_g$$^κ\text{CMB}\rangle + \langle^{γκ}$$_{\text{CMB}}\rangle$ changes from Stage-III (ongoing) to Stage-IV (future) surveys. Given the flexibility in selecting the lens galaxy sample, we also explore systematically the impact on cosmological constraints when we vary the redshift range and magnitude limit of the lens galaxies using mock galaxy catalogs. We find that in our setup, the contribution to cosmological constraints from $\langle δ_g$$^κ\text{CMB}\rangle$ and $\langle^{γκ}$$_{\text{CMB}}\rangle$ are comparable in the Stage-III data sets; but in Stage-IV surveys, the noise in$\langle δ_g$$^κ\text{CMB}\rangle$ becomes subdominant to cosmic variance, preventing $\langle δ_g$$^κ\text{CMB}\rangle$ to further improve the constraints. This implies that to maximize the cosmological constraints from future $\langle δ_g$$^κ\text{CMB}\rangle$ + $\langle^{γκ}$$_{\text{CMB}}\rangle$ analyses, we should focus more on the requirements on$^{γκ}$$_{\text{CMB}}\rangle$ instead of$\langle δ_g$$^κ\text{CMB}\rangle$. Furthermore, the selection of the lens sample should be optimized in terms of our ability to characterize its redshift or galaxy bias instead of its number density.

79 ASTRONOMY AND ASTROPHYSICS↗

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗

Chaconne: A Statistical Approach to Nonlocal Compression for Supervised Learning, Semi-Supervised Learning, and Anomaly Detection

This project developed a novel statistical understanding of compression analytics (CA), which has challenged and clarified some core assumptions about CA, and enabled the development of novel techniques that address vital challenges of national security. Specifically, this project has yielded the development of novel capabilities including 1. Principled metrics for model selection in CA, 2. Techniques for deriving/applying optimal classification rules and decision theory to supervised CA, including how to properly handle class imbalance and differing costs of misclassification, 3. Two techniques for handling nonlocal information in CA, 4. A novel technique for unsupervised CA that is agnostic with regard to the underlying compression algorithm, 5. A framework for semisupervised CA when a small number of labels are known in an otherwise large unlabeled dataset. 6. The academic alliance component of this project has focused on the development of a novel exemplar-based Bayesian technique for estimating variable length Markov models (closely related to PPM [prediction by partial matching] compression techniques). We have developed examples illustrating the application of our work to text, video, genetic sequences, and unstructured cybersecurity log files.

99 GENERAL AND MISCELLANEOUS↗

Microbial Biomining for the Release and Recovery of Rare Earth Elements in Abandoned Coal Mine Drainage

Microbes can be used for biomining rare earth elements (REEs) from abandoned coal-mine drainage (AMD) solids. Domestically Pennsylvania has ~11,000 abandoned mines, with ~500 AMD passive remediation systems (PRSs) that precipitate REE rich solids. In passive systems, REEs, co-precipitate with manganese (Mn), accumulating as a valuable leachate when resolubilized. REEs like lanthanum (La) are used in battery technology. Microbial metabolism that co-resolubilize Mn and REEs could result in an affordable release process that does not require the addition of hazardous chemical additives. Microbial sequestering of La can be used for selective purification from a mixed REE composition. Currently, the microbial mechanisms that contribute to mass REE resolubilization and selective sequestration are poorly understood. We have isolated bacteria (Bacillus mycoides JR07 and Bacillus pseudomycoides KB7) that solubilize Mn oxide, La oxide, and AMD solids by acidogenesis. Preliminary results show methylotrophic bacterial isolate B3 can take up soluble La, which may have a potential application in the purification of La from a mixed REE leachate. Determining the microbial metabolism and genes involved in the REE resolubilization and selective biomining of La is crucial to optimize the biomining of AMD solids. Our work addresses the growing need to develop novel REE recovery methods from domestic sources.

microbiology↗

Multi-Scale Modeling and Prototype Development for Electrochemical CO2 Reduction (CRADA Final Report)

In this CRADA project, Lawrence Livermore National Laboratory, Stanford University, SLAC National Laboratory, and TotalEnergies collaboratively executed a multidisciplinary investigation of electrochemical reduction of CO2 to produce sustainable fuels and chemicals. Overall, the project led to an increased understanding of the fundamental processes involved in CO2 electrolysis, from the atomistic scale to the full electrolyzer device scale, ultimately leading to design guidelines for CO2 electrolyzers that will help in their future commercialization. As the model systems, Ag- and Cu-based catalysts were investigated in various forms depending on the electrochemical platform that was utilized to study the activity, selectivity, and durability towards electrochemical CO2 reduction. By employing experimental, theoretical, and computational techniques, the project team experimentally validated multi-physics models, evaluated the experimental levers that lead to increased electrolyzer reaction selectivity and energy efficiency, and used computational optimization to design higher performance electrodes. The learnings of this project were extensively documented in publicly available peer-reviewed journal publications and conference presentations, which serve as a foundation for further work to build from.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Methods integrating innate and adaptive immune responses in human in vitro immunization assays

Rapid vaccine development and innovative immunotherapeutics are critical in the fight against emerging outbreaks and global pandemic threats, yet the high costs and prolonged timelines for developing new vaccines underscore the urgent need for robust, predictive pre-clinical testing platforms. The rapid down-selection of vaccine candidates and identification of optimal vaccine formulations can be performed using human in vitro immunization (IVI) assays that recapitulate the complex interactions of the innate and adaptive human immune response. In this review, we present a comprehensive evaluation of three key IVI platforms: the whole blood assay (WBA), monocyte-derived dendritic cell (MoDC) assay with dendritic cell-T cell interface assay (DTI), and the microphysiological human tissue construct assay (HTC). The WBA offers a cost-effective and straightforward approach, while the MoDC + DTI system represents the current gold standard for balancing experimental efficiency with immunological complexity. The HTC assay, by mimicking both spatial and temporal aspects of immune interactions, provides enhanced physiological relevance. We discuss the methodological advantages and limitations of each platform, explore their roles in rapid vaccine candidate screening, and propose strategies for integrating these assays with complementary in vivo models. These insights pave the way for refining IVI assays and accelerating the translational pipeline for next-generation vaccines and immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Chitosan Coating Functionalized with Flaxseed Oil and Green Tea Extract as a Bio-Based Solution for Beef Preservation

Ecological and safe packaging solutions arise as pivotal points in the development of an integrated system for sustainable meat production. The aim of this study was to assess the effect of a combined chitosan (Ch) + green tea extract (GTE) + essential oil (thyme oil, TO; flaxseed oil, FO; or oregano oil, OO) coating on the safety and quality of vacuum-packaged beef during storage at 4 °C. An optimized bio-based coating formulation was selected (2% Ch + 2% GTE + 0.1% FO) to be applied to three fresh beef cuts (shoulder, Sh; knuckle, Kn; Striploin, St) based on its pH (5.8 ± 0.1), contact angle (22.3 ± 0.4°) and rheological parameters (viscosity = 0.05 Pa.s at shear rate > 20 s−1). Shelf-life analysis showed that the Ch–GTE–FO coating delayed lipid oxidation and reduced total viable counts (TVC) and Enterobacteriaceae growth compared with uncoated beef samples over five days. In addition, Ch–GTE–FO coating decreased total color changes of beef samples (e.g., ∆E* = 9.84 and 3.94, for non-coated and coated Kn samples, respectively) for up to five days. The original textural parameters (hardness, adhesiveness and springiness) of beef cuts were maintained during storage when Ch–GTE–FO coating was applied. Based on the physicochemical and microbial characterization results, the combination of the Ch–GTE–FO coating developed was effective in preserving the quality of fresh beef cuts during refrigerated storage along with vacuum packaging.

Mendes, Cíntia G.↗

ALLOY DESIGN AND DEVELOPMENT OF HIGH CR CONTAINING FECRAL FERRITIC ALLOYS FOR EXTREME ENVIRONMENTS

A new alloy design concept for creep- and corrosion-resistant, fully ferritic alloys was proposed for high-temperature structural applications in current/future fossil-fired power plants. The alloys based on Fe-30Cr-3Al (in weight percent) with minor alloying additions of Nb, W, Si, Zr and/or Y were evaluated with a design strategy for corrosion resistance though high Cr content, steam oxidation resistance through alumina-scale formation, and high-temperature creep performance through fine particle dispersion of Fe2(Nb,W)-type Laves phase in the BCC-Fe matrix, which target to be used in harsh environments such as combustion and/or steam containing atmospheres, at or above 700°C. The alloys consisted of Fe-30Cr-3Al-1Nb-6W with minor alloying additions exhibited a successful combination of oxidation/corrosion/creep-resistant performances comparable or superior to those of commercially available heat resistant austenitic stainless steels. An optimized thermo-mechanical treatment combined with selected minor alloying addition resulted in a refined grain structure with high thermal stability even at 1200°C, which improved room-temperature ductility without sacrificing the creep performance. The mechanism of grain refinement in the alloy system has been discussed.

Yamamoto, Yukinori↗

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗

Beyond pinball loss: Quantile methods for calibrated uncertainty quantification

Amongthemanywaysofquantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A model that predicts the true conditional quantiles for each input, at all quantile levels, presents a correct and efficient representation of the underlying uncertainty. To achieve this, many current quantile-based methods focus on optimizing the pinball loss. However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e.g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles. In this work, we develop new quantile methods that address these shortcomings. In particular, we propose methods that can apply to any class of regression model, select an explicit balance between calibration and sharpness, optimize for calibration of centered intervals, and produce more accurate conditional quantiles. We provide a thorough experimental evaluation of our methods, which includes a high dimensional uncertainty quantification task in nuclear fusion.

97 MATHEMATICS AND COMPUTING↗

SU-F-T-337: Accounting for Patient Motion During Volumetric Modulated Ac Therapy (VMAT) Planning for Post Mastectomy Chest Wall Irradiation

Purpose: To evaluate two dose optimization strategies for maintaining target volume coverage of inversely-planned post mastectomy radiotherapy (PMRT) plans during patient motion. Methods: Five patients previously treated with VMAT for PMRT at our clinical were randomly selected for this study. For each patient, two plan optimization strategies were compared. Plan 1 was optimized to a volume that included the physician’s planning target volume (PTV) plus an expansion up to 0.3 cm from the bolus surface. Plan 2 was optimized to the PTV plus an expansion up to 0.3 cm from the patient surface (i.e., not extending into the bolus). VMAT plans were optimized to deliver 95% of the prescription to 95% of the PTV while sparing organs at risk based on clinical dose limits. PTV coverage was then evaluated following the simulation of patient shifts by 1.0 cm in the anterior and posterior directions using the treatment planning system. Results: Posterior patient shifts produced a difference in D95% of around 11% in both planning approaches from the non-shifted dose distributions. Coverage of the medial and lateral borders of the evaluation volume was reduced in both the posteriorly shifted plans (Plan 1 and Plan 2). Anterior patient shifts affected Plan 2 more than Plan 1 with a difference in D95% of 1% for Plan 1 versus 6% for Plan 2 from the non-shifted dose distributions. The least variation in PTV dose homogeneity for both shifts was obtained with Plan 1. However, all posteriorly shifted plans failed to deliver 95% of the prescription to 95% of the PTV. Whereas, only a few anteriorly shifted plans failed this criteria. Conclusion: The results of this study suggest both planning volume methods are sensitive to patient motion, but that a PTV extended into a bolus volume is slightly more robust for anterior patient shifts.

60 APPLIED LIFE SCIENCES↗