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DOE OSTI2025

2019 and 2022 Twin Cities Travel Behavior Inventory Surveys

# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2019 and 2022 Twin Cities Travel Behavior Inventory Surveys

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1973

The 100-micron observations of galactic and extragalactic Southern Hemisphere objects

Far-infrared observations of the interstellar medium are reported by a balloon-borne telescope. With the 30-centimeter (12-in.) telescope operating at a wavelength of 100 microns, approximately forty new sources were observed in the galactic plane and in the large Magellanic Cloud. Patterns and some degree of understanding are beginning to emerge from the observations of objects in the far infrared.

Hoffmann, W. F.↗

NASA NTRS2016

Pilot Field Test: Performance of a Sit-to-Stand Test After Long-Duration Space Flight

BACKGROUND: Astronauts returning from the International Space Station are met by a team of recovery personnel typically providing physical assistance and medical support immediately upon landing. That is because long-duration spaceflight impacts astronauts' functional abilities. Future expeditions to planets or asteroids beyond the low Earth orbit, however, may require crewmembers to egress the vehicle and perform other types of physical tasks unassisted. It is therefore important to characterize the extent and longevity of functional deficits experienced by astronauts in order to design safe exploration class missions. Pilot Field Test (PFT) experiment conducted with participation of ISS crewmembers traveling on Soyuz expeditions 34S - 41S comprised several tasks designed to study the recovery of sensorimotor abilities of astronauts during the first 24 hours after landing and beyond. METHODS: The first test in the PFT battery sequence, and also the least demanding one from the sensorimotor perspective, was a Sit-to-Stand test. Test subjects were seated in the chair and had to stand up on command and remain standing for ten seconds. The subjects were instructed to stand up unassisted as quickly as they were able to, while maintaining postural control. Synchronized wireless inertial sensors mounted on the head, chest, lower back, wrists, and ankles were used to continuously log body kinematics. Crewmembers' blood pressure and heart rate were monitored and recorded with the Portapres and Polar systems. Each session was recorded with a digital video camera. During data collections occurring within the 24-hour postflight period, crewmembers were also asked to (1) evaluate their perceived motion sickness symptoms on a 20-point scale before and after completion of the test and (2) estimate how heavy they felt compared to their normal (preflight) body weight. Consent to participate in PFT was obtained from 18 crewmembers (11 US Orbital Segment [USOS] astronauts and 7 Russian cosmonauts). For 10 subjects, the first set of data was collected in the medical tent in Soyuz landing zone (1-2 hours after landing); the other 8 subjects were tested at the Kazakhstan deployment airport (4-5 hours after landing). 8 of the 11 astronauts were tested twice more within the first 24 hours postflight, at a refueling stop on the way to Houston (approximately 13 hours after landing) and at the Johnson Space Center (approximately 24 hours after landing). Later postflight data were collected in the first two weeks on some crewmembers. Finally, 6 astronauts were tested 60+ days after landing to establish a delayed baseline. RESULTS/DISCUSSION: Two of the 18 PFT participants felt too ill to attempt any tests in Kazakhstan (at either the landing zone or deployment airport). The remaining test subjects completed the Sit-to-Stand test and their reported motion sickness scores were unaffected by this task. The task completion times and body kinematics data analysis are currently underway. Preliminary analysis of astronaut data shows a steep improvement in the time to complete the task on the second data take, and in some cases, the trend continues through day six postflight. Head and trunk pitch angles and pitch rates were also examined and increases in all measures are evident throughout the observed recovery period (60+ days postflight). Interesting patterns of head and trunk pitch coordination have also emerged. One of the data analysis objectives is comparison of initial postflight performance and recovery of experienced crewmembers and first-time fliers. Another one - possible differences in performance between USOS and Russian crewmembers.

Kofman, I. S.↗

NASA NTRS2022

Laser Patterning Assisted Devitrification and Domain Engineering of Amorphous and Nanocrystalline Alloys

We have investigated laser-patterning and devitrification as a novel method to realize emergent properties in modified regions at the ribbon surface consisting of periodic localized laser spots thermally treated under rapid heating and cooling conditions. Fe-based amorphous ribbons were annealed for systematically varied laser power. Extremely high heating and cooling rates on the order of 10(exp 8)-10(exp 10)K/s have been estimated by finite element analysis simulations. Observations suggest surface melting followed by rapid solidification fast enough to quench and form an amorphous structure upon cooling. Diffusion of heat occurs from the laser irradiated surface region so that a finite volume of material surrounding this region rises in temperature above the crystallization temperature relevant for conventional isothermal annealing experiments. The underlying mechanism of laser annealing and consequences of heat transients involved are discussed in terms of impacts upon micro/nanostructure, residual stresses, and magnetic domain structure surrounding the laser irradiated region. The study illustrates the potential to exploit spatially optimized phase transformations in a scalable manufacturing process of amorphous and nanocrystalline alloys to locally access otherwise inaccessible extreme heating and cooling rates.

Amorphous ribbons↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

DOE OSTI2025

Self-generated electrokinetic flows from active-charged boundary patterns

We develop a hydrodynamic description of self-generated electrolyte flow in capillaries whose bounding walls feature nonuniform distributions of charge nonuniform active ionic fluxes. The hydrodynamic velocity arising in such a system has components that are forbidden by symmetry in the absence of charge and fluxes. However, when these two boundary mechanisms are simultaneously present, they can lead to a symmetry broken state where steady flows with both unidirectional and circulatory components emerge. We show that these flow states arise when modulated boundary patterns of charge and fluxes are offset by a flux-charge phase difference, which is associated with the separation between sites of their peak densities on the wall. Mismatch in diffusivity of cationic and anionic species can modify the flow states and becomes an enhancing factor when fluxes of both ion species are being produced together at the same site. We demonstrate that this mechanism can be realized with a microfluidic generator that is powered by enzyme-coated patches that catalyze reactants in the solution to produce fluxes of ions. The local ionic elevation or depletion, which disrupts a nonuniform double layer, promotes self-induced gradients yielding persistent body forces to generate bulk fluid motion. Our work quantifies a boundary-driven mechanism behind self-sustained electrolyte flow in confined environments that exists without any external bulk-imposed fields or gradients. It provides a theoretical framework for understanding the combined effect of active and charged boundaries that are relevant in biological or soft matter systems, and can be utilized in electrofluidic and iontronic applications.

active matter↗

DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing al-location policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

Ranganath, Kiran↗

DOE OSTI2024

Direct electron beam patterning of electro-optically active PEDOT:PSS

The optical and electronic tunability of the conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) has enabled emerging applications as diverse as bioelectronics, flexible electronics, and micro- and nano-photonics. High-resolution spatial patterning of PEDOT:PSS opens up opportunities for novel active devices in a range of fields. However, typical lithographic processes require tedious indirect patterning and dry etch processes, while solution-processing methods such as ink-jet printing have limited spatial resolution. Here, we report a method for direct write nano-patterning of commercially available PEDOT:PSS through electron-beam induced solubility modulation. The written structures are water stable and maintain the conductivity as well as electrochemical and optical properties of PEDOT:PSS, highlighting the broad utility of our method. We demonstrate the potential of our strategy by preparing prototypical nano-wire structures with feature sizes down to 250 nm, an order of magnitude finer than previously reported direct write methods, opening the possibility of writing chip-scale microelectronic and optical devices. We finally use the high-resolution writing capabilities to fabricate electrically-switchable optical diffraction gratings. We show active switching in this archetypal system with >95 % contrast at CMOS-compatible voltages of +2 V and -3 V, offering a route towards highly-miniaturized dynamic optoelectronic devices.

42 ENGINEERING↗

NASA NTRS2003

Expression of Prox1 defines regions of the avian otocyst that give rise to sensory or neural cells

The simple primordium of the inner ear (otocyst) differentiates into many cell types, including sensory neurons and hair cells. We examined expression of the divergent homeobox transcription factor, cProx1, during otocyst development in chickens. Nuclear cProx1 protein is not evident in the otic placode but emerges in the otic cup by stage 12. At stage 16, cProx1-positive nuclei are scattered continuously throughout the neuroepithelium, from anteroventral to posteromedial. These labeled cells are neural precursors; they express betaIII-tubulin and migrate to the cochleovestibular ganglion between stages 13 and 21. By stage 18, two areas develop a dense pattern of cProx1 expression in which every nucleus is labeled. These areas emerge at the anterior and posterior extremes of the band of scattered cProx1 expression and express the sensory markers cSerrate1 and Cath1 by stage 23. Four discrete patches of dense cProx1 expression appear by stage 23 that correspond to the future superior crista, lateral crista, saccular macula, and posterior crista, as confirmed by immunolabeling for hair cell antigen (HCA) by stage 29. The remaining sensory epithelia display a dense pattern of cProx1 expression and label for HCA by stage 29. In the basilar papilla, nuclear cProx1 expression is down-regulated in most hair cells by stage 37 and in many supporting cells by stage 40. Our findings show that regions of the otocyst that give rise to neurons or hair cells are distinguished by their relative density of cProx1-positive nuclei, and suggest a role for cProx1 in the genesis of these cell types.

NASA Discipline Developmental Biology↗

DOE OSTI2023

Extending OpenSHMEM with Aggregation Support for Improved Message Rate Performance

OpenSHMEM is a highly efficient one-sided communication API that implements the PGAS parallel programming model, and is known for its low latency communication operations that can be mapped efficiently to RDMA capabilities of network interconnects. However, applications that use OpenSHMEM can be sensitive to point-to-point message rates, as many-to-many communication patterns can generate large amounts of small messages which tend to overwhelm network hardware that has predominantly been optimised for bandwidth over message rate. Additionally, many important emerging classes of problems such as data analytics are similarly troublesome for the irregular access patterns they employ. Message aggregation strategies have been proven to significantly enhance network performance, but their implementation often involves complex restructuring of user code, making them unwieldy. This paper shows how to combine the best qualities of message aggregation within the communication model of OpenSHMEM such that applications with small and irregular access patterns can improve network performance while maintaining their algorithmic simplicity. We do this by providing a path to a message aggregation framework called conveyors through a minimally intrusive OpenSHMEM extension introducing aggregation contexts that fit more naturally to the OpenSHMEM atomics, gets, and puts model. We test these extensions using four of the bale 3.0 applications which contain essential many-to-many access patterns to show how they can produce performance improvements of up to 65×.

Welch, Aaron↗

DOE OSTI2021

Domain Wall Patterning and Giant Response Functions in Ferrimagnetic Spinels

Abstract The manipulation of mesoscale domain wall phenomena has emerged as a powerful strategy for designing ferroelectric responses in functional devices, but its full potential is not yet realized in the field of magnetism. This work shows a direct connection between magnetic response functions in mechanically strained samples of Mn 3 O 4 and MnV 2 O 4 and stripe‐like patternings of the bulk magnetization which appear below known magnetostructural transitions. Building off previous magnetic force microscopy data, a small‐angle neutron scattering is used to show that these patterns represent distinctive magnetic phenomena which extend throughout the bulk of two separate materials, and further are controllable via applied magnetic field and mechanical stress. These results are unambiguously connected to the anomalously large magnetoelastic and magnetodielectric response functions reported for these materials, by performing susceptibility measurements on the same crystals and directly correlating local and macroscopic data.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dual phase patterning during a congruent grain boundary phase transition in elemental copper

The phase behavior of grain boundaries can have a strong influence on interfacial properties. Little is known about the emergence of grain boundary phases in elemental metal systems and how they transform. Here, we observe the nanoscale patterning of a grain boundary by two alternating grain boundary phases with distinct atomic structures in elemental copper by atomic resolution imaging. The same grain boundary phases are found by computational grain boundary structure search indicating a first-order transformation. Finite temperature atomistic simulations reveal a congruent, diffusionless transition between these phases under ambient pressure. The patterning of the grain boundary at room temperature is dominated by the grain boundary phase junctions separating the phase segments. Our analysis suggests that the reduced mobility of the phase junctions at low temperatures kinetically limits the transformation, but repulsive elastic interactions between them and disconnections could additionally stabilize the pattern formation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Surface and interface structures of epitaxial Sb 2 Se 3 on mica

Sb 2 Se 3 thin film is an emerging photon absorber used in solar cells. We report the study of surface and interface structures of Sb2Se3(1 2 0) film grown on mica substrate by a high-rate vapor transport method. The interface epitaxial relationship between Sb 2 Se 3 and mica examined by the cross- sectional TEM images and diffraction patterns along the [0 0 1] and [10] directions of Sb 2 Se 3 reveal a rectangular structure with lengths of 4.03 ± 0.1 Å and 5.29 ± 0.1 Å, consistent with the [1 2 0] out-of-plane direction of Sb 2 Se 3 bulk lattice parameters. In contrast, the two-dimensional reciprocal space map (2D map) constructed from azimuthal reflection high-energy electron diffraction (ARHEED) patterns from the surface exhibits a decorated hexagonal structure. This surface structure emerges from six epitaxial orientation domains/rods and each domain has a rectangular unit mesh of 3.94 ± 0.09 Å and 26.95 ± 1.16 Å along the [0 0 1] and [10] directions. The 26.95 Å is consistent with the unit mesh of the outermost layer of the Sb 2 Se 3 (1 2 0) domains/rods. Overall, our 2D map reveals surface information that are not easily observed by other diffraction techniques.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Steric Sea Level Rise and Relationships with Model Drift and Water Mass Representation in GFDL CM4 and ESM4

Abstract Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, intermodel differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spinup and preindustrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL CM4, are compared with a lower-resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only [i.e., Ocean Model Intercomparison Project (OMIP)] and coupled climate configurations. After correcting for model drift, both GFDL CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of Antarctic Intermediate Water (AAIW), Subpolar Mode Water (SPMW), and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea level–related model biases.

Krasting, John P. [a NOAA/OAR/Geophysical Fluid Dy↗

DOE OSTI2021

Spatial and temporal coevolution of N2 neuraminidase and H1 and H3 hemagglutinin genes of influenza A virus in US swine

Abstract The neuraminidase (NA) and hemagglutinin (HA) are essential surface glycoproteins of influenza A virus (IAV). In this study, the evolution of subtype N2 NA paired with H1 and H3 subtype HA in swine was evaluated to understand if the genetic diversity of HA and NA were linked. Using time-scaled Bayesian phylodynamic analyses, the relationships of paired swine N2 with H1 or H3 from 2009 to 2018 were evaluated. These data demonstrated increased relative genetic diversity within the major N2 clades circulating in swine in the USA (N2.1998 between 2014 and 2017 and N2.2002 between 2010 and 2016). Preferential pairing was observed among specific NA and HA genetic clades. Gene reassortment between cocirculating influenza A strains resulted in novel pairings that persisted. The changes in genetic diversity in the NA gene were quantified using Bayesian phylodynamic analyses, and increases in diversity were observed subsequent to novel NA–HA reassortment events. The rate of evolution among NA–N2 clades and HA–H1 and HA–H3 clades were similar. Bayesian phylodynamic analyses demonstrated strong spatial patterns in N2 genetic diversity, but frequent interstate movement of rare N2 clades provided opportunity for reassortment and emergence of new N2–HA pairings. The frequent regional movement of pigs and their influenza viruses is an explanation for the documented patterns of reassortment and subsequent changes in gene diversity. The reassortment and evolution of NA and linked HA evolution may result in antigenic drift of both major surface glycoproteins, reducing vaccine efficacy, with subsequent impact on animal health.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Topology optimization for the design of porous electrodes

Porous electrodes are an integral part of many electrochemical devices since they have high porosity to maximize electrochemical transport and high surface area to maximize activity. Traditional porous electrode materials are typically homogeneous, stochastic collections of small-scale particles and offer few opportunities to engineer higher performance. Fortunately, recent breakthroughs in advanced and additive manufacturing are yielding new methods to structure and pattern porous electrodes across length scales. These architected electrodes are emerging as a promising new technology to continue to drive improvement; however, it is still unclear which structures to employ and few tools are available to guide their design. Here we address this gap by applying topology optimization to the design of porous electrodes. We demonstrate our framework on two applications: a porous electrode driving a steady Faradaic reaction and a transiently operated electrode in a supercapacitor. We present computationally designed electrodes that minimize energy losses in a half-cell. For low-conductivity materials, the optimization algorithm creates electrode designs with a hierarchy of length scales. Further, the designed electrodes are found to outperform undesigned, homogeneous electrodes. Finally, we present three-dimensional porous electrode designs. We thus establish a topology optimization framework for designing porous electrodes.

25 ENERGY STORAGE↗