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At least 1,009 records · Page 56

Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Bramer, Lisa M.↗

Misanthropic entropy and renormalization as a communication channel

A central physical question is the extent to which infrared (IR) observations are sufficient to reconstruct a candidate ultraviolet (UV) completion. We recast this question as a problem of communication, with messages encoded in field configurations of the UV being transmitted to the IR degrees of freedom via a noisy channel specified by renormalization group (RG) flow, with noise generated by coarse graining/decimation. We present an explicit formulation of these considerations in terms of lattice field theory, where we show that the “misanthropic entropy” — the mutual information obtained from decimating neighbors — encodes the extent to which information is lost in marginalizing over/tracing out UV degrees of freedom. Our considerations apply both to statistical field theories as well as density matrix renormalization of quantum systems, where in the quantum case, the statistical field theory analysis amounts to a leading-order approximation. As a concrete example, we focus on the case of the 2D Ising model, where we show that the misanthropic entropy detects the onset of the phase transition at the Ising model critical point.

Physics↗

Bremsstrahlung from neutrino scattering via magnetic dipole moments

In this paper, we discuss bremsstrahlung induced by neutrino scattering. This process should exist since neutrinos are expected to couple to photons via magnetic dipole and transition moments. These moments are loop-induced and tiny in the Standard Model with neutrino masses but could be significantly enhanced in extended theories. As concrete example, we study the scattering of the two largest neutrino fluxes on earth, solar neutrinos and Cosmic Neutrino Background (CNB). Finally, it is tempting to consider this as a potential signature for CNB searches but it turns out that the signal is extremely small and unlikely to be observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

On the Abuse and Detection of Polyglot Files

A polyglot is a file that is valid in two or more formats. Polyglot files pose a problem for file-upload and generative AI web interfaces that rely on format identification to determine how to securely handle incoming files. In this work we found that existing file-format and embedded-file detection tools, even those developed specifically for polyglot files, fail to reliably detect polyglot files used in the wild. To address this issue, we studied the use of polyglot files by malicious actors in the wild, finding 30 polyglot samples and 15 attack chains that leveraged polyglot files. Using knowledge from our survey of polyglot usage in the wild---the first of its kind---we created a novel data set based on adversary techniques. We then trained a machine learning detection solution, PolyConv, using this data set. PolyConv achieves a precision-recall area-under-curve score of 0.999 with an F1 score of 99.20% for polyglot detection and 99.47% for file-format identification, significantly outperforming all other tools tested. We developed a content disarmament and reconstruction tool, ImSan, that successfully sanitized 100% of the tested image-based polyglots, which were the most common type found via the survey. Our work provides concrete tools and suggestions to enable defenders to better defend themselves against polyglot files, as well as directions for future work to create more robust file specifications and methods of disarmament.

Oesch, T [ORNL] (ORCID:0000000269091022)↗

Cross-Domain Reasoning for Neuromorphic Model Design

Designing performant neuromorphic models requires reasoning across neuroscience, neuromorphic computing, and machine learning, making it a natural target for cross-domain hypothesis generation. Our primary contribution is a multi-corpus knowledge graph spanning all three domains, which we show substantially increases cross-domain retrieval novelty over single-corpus baselines. We additionally introduce NeuKReAct, an agentic reasoning framework that iteratively retrieves from this graph and synthesizes design hypotheses via a step-by-step blackboard architecture, enabling structured compartmentalization of design decisions. Lastly, we introduce an execution head that translates hypotheses into structured design documents and runnable code. We evaluate novelty using a combinatorial creativity metric that measures cross-domain retrieval distance across the citation graph. Our results confirm that corpus breadth is the dominant driver of novelty. Moreover, we highlight a concrete instance of the novelty-utility tradeoff within NeuKReAct, underscoring a need for joint creativity evaluation, balancing both novelty and utility.

Ramavarapu, Vikram [ORNL] (ORCID:0009000188757213)↗

Study on Intelligent Compaction-Equipment Logistics Scheduling and Propagation Characteristics of Vibration Wave in Nonlinear Systems with Multistability Based on Field Test

The equipment scheduling and propagation characteristics of vibration wave from vibratory roller ⟶ filling material nonlinear systems with multistability are the core problems of subgrade intelligent construction technology, and the logistics scheduling of the equipment is directly related to the construction efficiency. Aiming at the shortages, one typical subgrade located at the Gu’an station of Beijing-Xiong’an city railway is selected to research and finish the field tests; some findings are shown as follows: first, some valuable suggestions about the logistics scheduling of intelligent equipment are proposed, which can break the barriers between the organizations and improve construction efficiency; second, when the vibration wave propagates from the vibratory roller ⟶ surface of filling material ⟶ different buried depths of filling material, the peak acceleration of vibration wave gradually decreases and is hyperbolic distribution approximately. At the same time, the sensitive of attenuation is shown as follows: Z R 2 >0.9), and the concrete functional relationship among different amplitudes of harmonics can be summarized as y = Ae −BX ; fourth, the vibration energy is mainly concentrated near 10–30 Hz in the vibratory roller, but when the vibration wave propagates from vibratory roller⟶filling material, the vibration energy gradually decreases with the increase in depth, and the marginal spectrum gradually changes from one peak to two peaks, that is, 30–50 Hz and 50–100 Hz; fifth, the vibration energy in the vibrational wheel is distributed averagely in the compaction process, and the effective compaction time is two seconds, which will be helpful for revealing the propagation characteristics of vibration wave, optimizing the compaction quality control models and providing some support for the development of intelligent compaction theory of railway subgrade.

Han, Yixuan↗

Microstructural Study of Hydration of C 3 S in the Presence of Calcium Nitrate Using Scanning Transmission X-Ray Microscopy (STXM)

Calcium nitrate (CN) is used widely as an effectively inorganic setting accelerator and antifreeze admixture in concrete structures. In this paper, the multiscale investigation of CN on the hydration of C 3 S was studied by scanning transmission X-ray microscopy (STXM) combined with near-edge X-ray absorption fine structure (NEXAFS), 29 Si MAS NMR, calorimetry, scanning electron microscope, and N 2 absorption. It was concluded that the calcium silicate hydrate (C-S-H) surrounds the unhydrated C 3 S at 1-day hydrated C 3 S in the presence of calcium nitrate, while portlandite is partly in transformation and is formed partly. Based on Ca L 3 , 2 -edge NEXAFS spectra for 1-day hydrated C 3 S particle, calcium nitrate does not change the structure of the asymmetrically 7-fold coordination of calcium in the C-S-H. Calcium nitrate can accelerate the hydration of C 3 S to some extent and polymerization of the silicate chains within C-S-H considerably at early age, resulting in the increasing specific surface area.

Li, Qinfei↗

Learning Forecasts of Rare Stratospheric Transitions from Short Simulations

Abstract Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.

Finkel, Justin↗

Low-velocity impact resistance and failure characteristics of all thermoplastic woven polymer-fiber-reinforced plastic composites

This study addresses the impact performances of recyclable composites made of all thermoplastic polymer-fiber-reinforced plastics (PFRPs), where the reinforcing fibers and matrix are made of thermoplastic polymers. Three woven PFRPs systems were evaluated, including polypropylene fibers, polypropylene matrix, and high-density polyethylene matrix. In low-velocity impact scenario with an impactor speed of less than 6 m/s, our results demonstrate the energy absorption capabilities of the flat laminate PFRPs compared to woven carbon fiber-reinforced plastics (CFRPs) and aluminum alloy 5052. For the systems studied, the PFRPs can reach the specific energy absorption 89% to 115% of the CFRPs. Even compared with the aluminum alloy 5052, the PFRPs can reach up to 97%. We investigate the failure morphologies of the PFRPs using X-ray µCT scans. They reveal the PFRPs’ unique ductile failure morphologies compared to common CFRPs. In addition, we heal the perforated region in the PFRPs by applying the manufacturing process identical to the initial curing process. The healed panels are perforated again, and they recovered 30% to 38% of their original specific energy absorption, a recovery not achievable with CFRPs. This study provides valuable experimental results, and concrete insights into the potential applications of recyclable PFRPs in various engineering fields. It emphasizes their excellent energy-absorbing capability and repairability.

CFRPs↗

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗

Resiliency in numerical algorithm design for extreme scale simulations

Here this work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 10 23 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

79 ASTRONOMY AND ASTROPHYSICS↗

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

54 ENVIRONMENTAL SCIENCES↗

Carbon-Storing Structural Material Based on Wastewater-Cultivated Chlorella Sorokiniana

Microalgae cultivation is a promising technology for carbon sequestration and wastewater remediation. It is economically and logistically critical to find viable applications for the large quantities of produced biomass. In current research, a structural material is developed by using nearly 100% Chlorella sorokiniana. The otherwise non-cohesive microalgae cells are first surface-activated and then compacted under a relatively high pressure. The resulting material is a dense solid, stronger than typical steel-reinforced concrete in flexural tests. The processing procedure is simple and fast, and the setup is scalable. This technique may be useful for next-generation green construction and provide a use-case for sustainable microalgae biomass.

Yi, Haozhe↗

Conducting more inclusive solar geoengineering research: A feminist science framework

Solar geoengineering, or deliberate climate modification, has been receiving increased attention in recent years. Given the far-reaching consequences of any potential solar geoengineering deployments, it is prudent to identify inherent biases, blind spots, and other potential issues at all stages of the research process. Here we articulate a feminist science-based framework to concretely describe how solar geoengineering researchers can be more inclusive of different perspectives and potentially contradictory conclusions, in the process illuminating potential implicit bias and enhancing the conclusions that can be gained from their studies. Importantly, this framework is an adoptable method of practice that can be refined, with the aim of conducting better research in solar geoengineering. As an illustration, we retrospectively apply this framework to a well-read solar geoengineering study (also led by the first author of this study), improving transparency by revealing its implicit values, conclusions made from its evidence base, and the methodologies that study pursues. We conclude here with a set of recommendations for the geoengineering research community whereby more inclusive research can become a regular part of practice. Throughout this process, we illustrate how feminist science scholars can use this approach to study climate modeling.

54 ENVIRONMENTAL SCIENCES↗

Overview of leakage scenarios in supervised machine learning

Machine learning (ML) provides powerful tools for predictive modeling. ML’s popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines.

97 MATHEMATICS AND COMPUTING↗

DPSIR-ESA Vulnerability Assessment (DEVA) Framework: Synthesis, Foundational Overview, and Expert Case Studies

Land resources are central to understanding the relationship between humans and their environment. We broadly define a land resource to include all the ecological resources of climate, water, soil, landforms, flora, and fauna, and all the socioeconomic systems that interact with agriculture, forestry, and other land uses within some system boundary. Understanding the vulnerability of land resources to changes in land management or climate forcing is critical to developing sustainable land management strategies. Vulnerability assessments are complex given the multiple uses of the assessments, the multi-disciplinary nature of the problem, limited understanding, the dynamic structure of vulnerability, scale issues, and problems with identifying effective vulnerability indicators. Here, we propose a novel conceptual framework for vulnerability assessments of land resources that combines the driver– pressure–state–impact–response (DPSIR) framework adopted by the European Environment Agency to describe interactions between society and the environment, and the exposure-sensitivity-adaptive capacity (ESA) framework used by the Intergovernmental Panel on Climate Change to assess impacts of climate change. The DPSIR-ESA Vulnerability Assessment (DEVA) framework operationalizes the process of assessing the vulnerability of a target system to external stressors. The DEVA framework includes the following elements: 1) Definition of the target system (Land resource), 2) Description of internal characteristics of the target system (State), 3) Description of target system vulnerability indicators (Adaptive capacity, Sensitivity), 4) Description of stressor characteristics (Drivers, Pressures), 5) Description of stressor vulnerability indicators (Exposure), 6) Description of target system response to stressors (Impacts), and 7) Description of modifications to target systems or stressors (Responses). In stating that they have “applied the DEVA framework”, analysts acknowledge that they have (a) considered the full breadth of each DEVA element, (b) have made conscious decisions to limit the scope and complexity of certain elements, and (c) can communicate both the rationale for these decisions and the impact of these decisions on the vulnerability assessment results and recommendations. The DEVA framework was refined during invited presentations and follow-up discussions from a series of Special Sessions with leading experts at two successive ASABE Annual International Meetings. Six case studies drawn from the sessions elaborate upon the DEVA framework and provide concrete examples of the key concepts. The DEVA approach gives engineers, planners, and analysts a new, flexible framework to apply a broad array of useful tools toward assessment of land resource system vulnerability.

Anandhi, Aauvadi↗

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗

CROCUS Urban Fluxes of CO₂, H₂O, and Turbulence at University of Illinois Chicago

As of May 12, 2026 this dataset is currently being versioned to include data up to April 2026. Once the versioning process is complete, new data files will be available for access. The dataset metadata will also be updated to reflect the data availability of the new data being versioned. This dataset was collected at the UIC Plant Research Laboratory in Chicago, Illinois, as part of the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory. The site provides continuous atmospheric flux measurements, focusing on CO₂, H₂O, and heat and momentum transport in an urban setting. The data is processed at 30 minutes interval using the Eddy Covariance method and includes quality control and diagnostic data generated by EddyPro software. The data is stored in the netCDF files following CF conventions. The UIC Plant Research Laboratory is located near major highways and urban infrastructure, including buildings and parking areas. The surrounding landscape consists of a mix of turf, plants, trees, and impervious surfaces such as concrete and asphalt, making it ideal for studying urban at for studies on urban sustainability, air quality, and the effects of urbanization on atmospheric processes on urban climate dynamics, air quality, and surface-atmosphere exchanges within the city of Chicago. This dataset is funded by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research (BER) program.

54 ENVIRONMENTAL SCIENCES↗