Negotiating with solvers: Using Generalized Disjunctive Programming to find Computationally Performant MIP Formulations
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Davis, Soren Arthur [Sandia National Laboratories ↗
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Energy Resilience Options for the Koolauloa Community Resilience Hub – Energy Technology Innovation Partnership Project, Cohort 2: Summary of Findings and Assessment
Hui o Hau‘ula (HoH) is a community organization dedicated to the well-being of the population of the Ko‘olauloa district on the Hawaiian island of Oahu. In response to growing concerns about challenges related to extreme weather events or natural disasters, HoH formulated the concept of the Ko‘olauloa Community Resilience Hub, or KCRH. The KCRH facility would serve as a focal point for the community during normal conditions, while also providing essential services and acting as a safe space during emergencies, natural or man-made. The concept of the KCRH was initially developed in partnership with the Hawaii Natural Energy Institute, +Lab Architects, and the City and County of Honolulu. In the fall of 2023, the U.S. Department of Energy, under the Energy Technology Innovation Partnership Project Program (ETIPP), provided support to the KCRH project, in the form of technical assistance (TA) to be provided by its National Laboratory complex. In this case, the core TA was provided by Sandia National Laboratories, and it was directed to providing options for designing an energy system based at the KCRH that could support critical loads in the event of a 30- day grid outage. The National Renewable Energy Laboratory (NREL) provided communications and logistics support in the project.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation
This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.
Blakely, Benjamin [Argonne National Laboratory (AN↗
DAOS Benchmarks and Findings
We benchmark DAOS on a 127-node, 4,064-target pool using fio, IOR, IO500, and mdtest, comparing the DFS API against DFuse+POSIX. Single-client fio sweeps show larger block sizes and writes yield higher bandwidth, with similar random/sequential performance. Multi-node IOR (1–32 nodes, 6–48 tasks/node) shows bandwidth saturating around 32 tasks/node, with large transfer sizes hurting read throughput at high concurrency and write latency growing faster than read latency. An 8-node IO500 run shows DFS achieving ~5x higher bandwidth and ~190x higher IOPS than POSIX. We also detail operational issues: SCM target-filling under mdtest, intermittent NA_HOSTUNREACH errors, fio CPU-affinity failures, and a pool error storm resolved by recreation.
George, Rebecca [Thomas Jefferson National Acceler↗
Noisy Weights: New Perspectives on Finding Noise-Robust Flat Minima
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Jacobson, Philip Long [Sandia National Laboratorie↗
Finding the Adhesion Sweet Spot
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Engineering↗
Finding secondary mutations in Thermoanaerobacterium saccharolyticum strains with pyruvate ferredoxin oxidoreductase (PFOR) deletions.
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Olson, Daniel↗
Finding values in lignin: A promising yet under-utilized component of the lignocellulosic biomass
This article outlines the technical and economic potentials of lignin in unlocking sustainable biorefineries. The benefits of using this highly functionalized biopolymer for the growth of sustainable economy have been highlighted. But practically, the possibility of commercially substituting petroleum oil with lignin is still not very high as the estimated biofuel production cost is 2–3 times higher than the former one. However, with the advancement in technology and more efficient measures by biorefineries such as storing and processing the biomass near the field so as to reduce the transportation cost, it is possible to gain higher profits. Companies like Domtar, Stora Enso, Borregaard’s LignoTech, VITO, and Chemelot InSciTe have been promoting commercial value of lignin. The growth of lignin market after the start-up production at various sites has been discussed in this review. Combining the complete “start-to-finish” analysis with economic evaluation gives a pragmatic overview of the possibilities whether lignin will join petroleum oil as an efficient and cost-effective renewable source.
Jassal, Vidhisha↗
Shifting Trade-offs: Finding the Sustainable Nexus of Hydropower and Environmental Flows in the San Joaquin River Watershed, California
Environmental flow management in watersheds with multi-objective reservoirs is often presented as an additional constraint to an already strained and over-allocated stream system. Nevertheless, environmental flow legislation and regulatory policies are increasingly being developed and implemented globally. In California, USA, recent legislative and regulatory policies place environmental flows at the forefront of the state’s water management objectives; however, the increased reliance on hydropower to support climate change mitigation goals may complicate efforts on both issues. This study modelled alternative environmental flow strategies in the major tributaries to the San Joaquin River in California. Strategies included detailed water management rules for hydropower production, flood control, and water deliveries, and three methodological approaches to environmental flow releases: minimum instream flows (“baseline”) year-round, 40% of full natural flow (FNF) during the spring runoff season and minimum releases the remainder of the year, and functional flows year-round. Results show that environmental flow strategies affect downstream flow releases in each of the San Joaquin’s four sub-basins differently depending on infrastructure capacity, water management objectives, and hydrologic year types. While hydropower production was comparable or declined in the Stanislaus, Tuolumne, and Merced basins, functional flow and 40% FNF strategies increased hydropower production in the Upper San Joaquin basin by 11%. Uncontrolled spill of high flow events decreased when high flow releases were based on hydrologic cues rather than exclusively on flood storage capacity. Water deliveries were reduced in all years regardless of environmental flow strategy. The 40% FNF and functional flow strategies both increased water released to the river relative to baseline, but in different ways. The functional flow strategy allocated water in a holistic approach that enhanced ecological functions in all years, but particularly in moderate and wet years. In contrast, the 40% FNF strategy provided increased flows relative to baseline and some ecological benefit in dry years, but less ecological benefit in other years. This study shows that alternative environmental flow strategies will have different and important trade-offs for integrated water management, and may mutually benefit seemingly conflicting objectives.
13 HYDRO ENERGY↗
Finding new perovskite halides via machine learning
Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.
36 MATERIALS SCIENCE↗
Healthcare Providers’ Perceptions and Self-Reported Fall Prevention Practices: Findings from a Large New York Health System
Among older adults, falls are the leading cause of injury-related deaths and emergency department visits, and the incidence of falls in the United States is rising as the number of older Americans increases. Research has shown that falls can be reduced by modifying fall-risk factors using multifactorial interventions implemented in clinical settings. However, the literature indicates that many providers feel that they do not know how to conduct fall-risk assessments or do not have adequate knowledge about fall prevention. To help healthcare providers incorporate older adult fall prevention (i.e., falls risk assessment and treatment) into their clinical practice, the Centers for Disease Control and Prevention’s (CDC) Injury Center has developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool kit. This study was conducted to identify the practice characteristics and providers’ beliefs, knowledge, and fall-related activities before they received training on how to use the STEADI tool kit. Data were collected as part of a larger State Fall Prevention Project funded by CDC’s Injury Center. Completed questionnaires were returned by 38 medical providers from 11 healthcare practices within a large New York health system. Healthcare providers ranked falls as the lowest priority of five conditions, after diabetes, cardiovascular disease, mental health, and musculoskeletal conditions. Less than 40% of the providers asked most or all of their older patients if they had fallen during the past 12 months. Less than a quarter referred their older patients to physical therapists for balance or gait training, and <20% referred older patients to community-based fall prevention programs. Less than 16% reported they conducted standardized functional assessments with their older patients at least once a year. These results suggest that implementing the STEADI tool kit in clinical settings could address knowledge gaps and provide the necessary tools to help providers incorporate fall-risk assessment and treatment into clinical practice.
60 APPLIED LIFE SCIENCES↗
Estimation of the Chances to Find New Phenomena at the LHC in a Model-Agnostic Combinatorial Analysis
In this paper, we estimate the number of event topologies that have the potential to be produced in 𝑝𝑝 collisions at the Large Hadron Collider (LHC) without violating kinematic and other constraints. We use numerical calculations and combinatorics, guided by large-scale Monte Carlo simulations of Standard Model (SM) processes. Then, we set the upper limit on the probability that new physics may escape detection, assuming a model-agnostic approach. The calculated probability is unexpectedly large, and the fact that LHC has not found new physics until now is not entirely surprising. Theoretical limitations and experimental challenges in observing new physics within the studied exclusive event classes are examined.
BSM↗
Histological Findings in Wild Rodents of the Great Basin
In the western United States, significant environmental change is expected through the combined effects of climate change, tree mortality, drought, and invasive species. Wild animals can be used as indicators of threats to populations and even entire communities by looking for histopathological changes in their tissues. This method may identify threats to wild populations quickly and more accurately than waiting to observe population declines; however, baseline data are needed for comparisons. In this work, rodents are surveyed at 5 sites in the Great Basin to record histopathological changes. Additionally, one site is next to a copper mine that was in operation for nearly 100 years. This site is compared to the other more pristine sites. Liver, lung, heart, and kidney tissues of 143 rodents from 10 species were processed and examined using standard histological methods. Thirty percent of rodents had histopathological changes. The most common histopathological changes were extramedullary hematopoiesis (11.9%), hyperplastic BALT (6.3%), and pyelitis (4.9%). Two pathogens were found: the nematode Capillaria hepatica (0.7%) and the fungus Emmonsia crescens (2.8%). The most common rodent captured, Peromyscus maniculatus (n = 116), was used to determine whether sex and mass predict histopathological changes. Body mass, not sex, was the best predictor, suggesting that heavier, older individuals should be selectively sampled if the goal is to assess potential threats to rodent populations. Data such as these can be compared to future data to assess populations and communities for effects of environmental change.
59 BASIC BIOLOGICAL SCIENCES↗
Finding Fast Transients in Real Time Using a Novel Light-curve Analysis Algorithm
The current data acquisition rate of astronomical transient surveys and the promise for significantly higher rates in the next decade necessitate the development of novel approaches to analyze astronomical data sets and promptly detect objects of interest. The Deeper, Wider, Faster (DWF) program is a survey focused on the identification of fast-evolving transients, such as fast radio bursts, gamma-ray bursts, and supernova shock breakouts. It employs multifrequency simultaneous coverage of the same part of the sky over several orders of magnitude. Using the Dark Energy Camera mounted on the 4 m Blanco telescope, DWF captures a 20 s g -band exposure every minute, at a typical seeing of ~1'' and an air mass of ~1.5. These optical data are collected simultaneously with observations conducted over the entire electromagnetic spectrum—from radio to γ -rays—as well as cosmic-ray observations. In this paper, we present a novel real-time light-curve analysis algorithm, designed to detect transients in the DWF optical data; this algorithm functions independently from, or in conjunction with, image subtraction. We present a sample of fast transients detected by our algorithm, as well as a false-positive analysis. Our algorithm is customizable and can be tuned to be sensitive to transients evolving over different timescales and flux ranges.
79 ASTRONOMY AND ASTROPHYSICS↗
An Enhanced Method for Scheduling Observations of Large Sky Error Regions for Finding Optical Counterparts to Transients
The discovery and subsequent study of optical counterparts to transient sources is crucial for their complete astrophysical understanding. Various gamma-ray burst (GRB) detectors, and more notably the ground-based gravitational wave detectors, typically have large uncertainties in the sky positions of detected sources. Searching these large sky regions spanning hundreds of square degrees is a formidable challenge for most ground-based optical telescopes, which can usually image less than tens of square degrees of the sky in a single night. We present algorithms for better scheduling of such follow-up observations in order to maximize the probability of imaging the optical counterpart, based on the all-sky probability distribution of the source position. We incorporate realistic observing constraints such as the diurnal cycle, telescope pointing limitations, available observing time, and the rising/setting of the target at the observatory’s location. We use simulations to demonstrate that our proposed algorithms outperform the default greedy observing schedule used by many observatories. Our algorithms are applicable for follow-up of other transient sources with large positional uncertainties, such as Fermi -detected GRBs, and can easily be adapted for scheduling radio or space-based X-ray follow-up.
79 ASTRONOMY AND ASTROPHYSICS↗
Fantastic Striations and Where to Find Them: The Origin of Magnetically Aligned Striations in Interstellar Clouds
Thin, magnetically aligned striations of relatively moderate contrast with the background are commonly observed in both atomic and molecular clouds. They are also prominent in MHD simulations with turbulent converging shocks. The simulated striations develop within a dense, stagnated sheet in the midplane of the post-shock region where magnetically induced converging flows collide. We show analytically that the secondary flows are an inevitable consequence of the jump conditions of oblique MHD shocks. They produce the stagnated, sheet-like sub-layer through a secondary shock when, roughly speaking, the Alfvénic speed in the primary converging flows is supersonic, a condition that is relatively easy to satisfy in interstellar clouds. The dense sub-layer is naturally threaded by a strong magnetic field that lies close to the plane of the sub-layer. The substantial magnetic field makes the sheet highly anisotropic, which is the key to the striation formation. Specifically, perturbations of the primary inflow that vary spatially perpendicular to the magnetic field can easily roll up the sheet around the field lines without bending them, creating corrugations that appear as magnetically aligned striations in column density maps. On the other hand, perturbations that vary spatially along the field lines curve the sub-layer and alter its orientation relative to the magnetic field locally, seeding special locations that become slanted overdense filaments and prestellar cores through enhanced mass accumulation along field lines. In our scenario, the dense sub-layer, which is unique to magnetized oblique shocks, is the birthplace for both magnetically aligned diffuse striations and massive star-forming structures.
79 ASTRONOMY AND ASTROPHYSICS↗
Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Ly α systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel -1 . Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.
79 ASTRONOMY AND ASTROPHYSICS↗
Finding Multiply Lensed and Binary Quasars in the DESI Legacy Imaging Surveys
The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant ( H 0 ). To achieve H 0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg 2 of the extragalactic sky observed in three optical bands ( g , r , and z ), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ~5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.
79 ASTRONOMY AND ASTROPHYSICS↗