SRI International

One of the great challenges of studying the coupled ionosphere-thermosphere system is the difficulty of making distributed in situ measurements simultaneously. Beginning with Birkeland's (1908) pioneering study of ionospheric currents via equivalent currents, many investigations of the complex, coupled, three-dimensional ionosphere-thermosphere system represent this system as a slab or thin shell either out of expedience or necessity. Almost all existing methods for assimilative reconstruction of ionosphere-thermosphere electrodynamics are based on such thin-shell representations. In this study, we use rocket-based measurements of neutral wind profiles and incoherent scatter radar measurements to directly calculate central height-integrated quantities in IT electrodynamics (perpendicular current, Joule heating, Hall and Pedersen conductance) and compare with estimates based on height-integrated quantities derived from the height-integrated Ohm's law and the expression for height-integrated Joule heating. It is shown that when an appropriate estimate of the neutral wind is included, estimates of these height-integrated quantities lie within ~20% of their true values. When the neutral wind is ignored (i.e., assumed to be zero in Earth's corotating frame of reference) estimates differ from their true values by as much as 100%.

Abstract On Boxing Day, 1838, Jacobi found a solution to the problem of geodesics on a triaxial ellipsoid, with the course of the geodesic and the distance along it given in terms of one-dimensional integrals. Here, a numerical implementation of this solution is described. This entails accurately evaluating the integrals and solving the resulting coupled system of equations. The inverse problem, finding the shortest path between two points on the ellipsoid, can then be solved using a similar method as for biaxial ellipsoids.

Millions of children worldwide are experiencing prolonged symptoms after SARS-CoV-2 infection, yet social risk factors for developing long COVID are largely unknown. As child health is influenced by the environment in which they live and interact, adverse social determinants of health (SDOH) may contribute to the development of pediatric long COVID. To identify whether adverse SDOH are associated with increased odds of long COVID in school-aged children and adolescents in the US. This cross-sectional analysis of a multicenter, longitudinal, meta-cohort study encompassed 52 sites (health care and community settings) across the US. School-aged children (6-11 years; n = 903) and adolescents (12-17 years; n = 3681) with SARS-CoV-2 infection history were included. Those with an unknown date of first infection, history of multisystem inflammatory syndrome in children, or symptom surveys with less than 50% of questions completed were excluded. Participants were recruited via health care systems, long COVID clinics, fliers, websites, social media campaigns, radio, health fairs, community-based organizations, community health workers, and existing research cohorts from March 2022 to August 2024, and surveys were completed by caregivers between March 2022 and August 2024. Twenty-four individual social determinant of health factors were grouped into 5 Healthy People 2030 domains: economic stability, social and community context, caregiver education access and quality, neighborhood and built environment, and health care access and quality. Latent classes were created within each domain and used in regression models. Presence of long COVID using caregiver-reported, symptom-based, age-specific research indices. The mean (SD) age among 4584 individuals included in this study was 14 (3) years, and 2330 (51%) of participants were male. The number of latent classes varied by domain; the reference group was the class with the least adversity. In unadjusted analyses, most classes in each domain were associated with higher odds of long COVID. After adjusting for many factors, including age group, sex, timing of infection, referral source, and other social determinant of health domains, economic instability characterized by difficulty covering expenses, poverty, receipt of government assistance, and food insecurity were associated with an increased risk of having long COVID (class 2 adjusted odds ratio [aOR], 1.57; 95% CI, 1.18-2.09; class 4 aOR, 2.39; 95% CI, 1.73-3.30); economic instability without food insecurity (class 3) was not (aOR, 0.93; 95% CI, 0.70-1.23). Poorer social and community context (eg, high levels of discrimination and low social support) was also associated with long COVID (aOR, 2.17; 95% CI, 1.77-2.66). Sensitivity analyses stratified by age group and adjusted for race and ethnicity did not alter or attenuate these results. In this study, economic instability that included food insecurity and poor social and community context were associated with greater odds of pediatric long COVID. Those with food security, despite experiencing other economic challenges, did not have greater odds of long COVID. Further study is needed to determine if addressing SDOH factors can decrease the rate of pediatric long COVID.

Functional network connectivity (FNC) among large-scale brain networks-including the default mode (DMN), frontoparietal (FPN), and salience (SN) networks-have been increasingly implicated in transdiagnostic features of mental health disorders. In this study, we examined FNC patterns among the DMN, FPN, SN, and nine additional large-scale networks using resting-state functional MRI (rs-fMRI) data from 7760 adolescents (ages 10-13) from the Adolescent Brain Cognitive Development (ABCD) study. We investigated whether altered connectivity among these networks was associated with symptoms of social anxiety, as reported by caregivers at the two-year follow-up visit. Bayesian multiple regression revealed small, positive associations between social anxiety symptoms and FNC between the SN and cingulo-opercular network (β=0.038, 95% HDI=[0.003, 0.073]), the SN and retrosplenial temporal network (β=0.031, 95% HDI=[0.001, 0.061]), and the DMN and dorsal attention network (β=0.046, 95% HDI=[0.008, 0.085]). Female sex was also associated with greater social anxiety (β=0.073, 95% HDI=[0.026, 0.119]). These results highlight specific patterns of FNC that may serve as early neurobiological markers of social anxiety during adolescent development, offering insight into the network-level mechanisms that underlie risk for social anxiety in youth. SIGNIFICANCE STATEMENT: By analyzing resting-state fMRI data from 7760 adolescents, we identified small, positive effects of salience network-cingulo-opercular network and salience network-retrosplenial temporal network connectivity on social anxiety, and a small, positive effect of default mode network-dorsal attention network connectivity. These findings highlight the role of salience and default mode network dynamics in shaping adolescent social anxiety risk. With growing interest in transdiagnostic approaches to mental health, our results provide novel evidence that network-level variations can represent a core risk factor for social anxiety during early adolescence. This work suggests that functional network connectivity could yield clinically relevant biomarkers for early identification and targeted interventions for social anxiety in adolescence.

Designing high-performance parts increasingly requires not just choosing a shape, but also tailoring what material goes where inside that shape. Additive manufacturing technologies are making such spatial control of material properties more and more feasible, opening the door to designs that were once impossible. Yet the palette of materials typically available to designers is limited and fixed, while the full range of conceivable materials is vast—and often uncertain. This raises a fundamental challenge: how do we optimize a part’s material distribution when many of the materials that might be ideal do not yet exist, or their feasibility is unknown? In this talk, we frame material properties themselves as design variables, co-optimizing their spatial distribution together with the part’s performance objectives. The key difficulty is constraining this search to materials that are actually possible. We address this through a feasibility function that quantifies the likelihood that a given combination of material properties can be realized, enabling designers to balance ambition against risk. This strategy makes it possible to explore ambitious design spaces—such as graded or hybrid materials—while still grounding the results in manufacturable reality. We illustrate the approach through examples in mechanical and thermal design, showing how feasibilityguided optimization produces new insights into “what material to place where.”

Abstract Advances in high-resolution, high-throughput measurement techniques, coupled with powerful recent developments in computational modeling, simulation, and artificial intelligence (AI), have enabled breakthroughs in rapid discovery and testing of novel materials. These possibilities raise new questions pertaining to the credibility of the underlying models for real-world use, especially when these models are adopted to inform engineering or regulatory decisions in applications that deal with human safety and life. We present a risk-informed credibility assessment framework, that has been applied in adjacent domains to support engineering and regulatory decision-making based on the use of computational (including AI) models, and demonstrate its application in the context of rapid discovery and testing of metallic alloys. The framework consists of a number of steps and best practices to define a question of interest, establish a context of use, assess the model risk, develop and execute a plan to validate and verify the model’s credibility within that context, document the results and deviations from the plan, determine adequacy of the models based on such results, and iterate over these steps until the required adequacy is achieved. We present a few case studies using a number of models developed to support rapid discovery and testing of novel metallic alloys in DARPA METALS. We highlight challenges, gaps, and opportunities for future directions that are generalizable to broader engineering applications.

Shared network testbeds are critical for systems and networking research. However, their shared hardware can introduce variability—like increased jitter or loss—that may impact experiment fidelity or reproducibility.

On Boxing Day, 1838, Jacobi found a solution to the problem of geodesics on a triaxial ellipsoid, with the course of the geodesic and the distance along it given in terms of one-dimensional integrals. Here, a numerical implementation of this solution is described. This entails accurately evaluating the integrals and solving the resulting coupled system of equations. The inverse problem, finding the shortest path between two points on the ellipsoid, can then be solved using a similar method as for biaxial ellipsoids.

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public repositories. These models are often used directly or integrated in product pipelines with no particular precautions, since they are effectively just data in tensor form and considered safe. In this paper, we raise awareness of a new machine learning supply chain threat targeting neural networks. We introduce MaleficNet 2.0, a novel technique to embed self-extracting, self-executing malware in neural networks. MaleficNet 2.0 uses spread-spectrum channel coding combined with error correction techniques to inject malicious payloads in the parameters of deep neural networks. MaleficNet 2.0 injection technique is stealthy, does not degrade the performance of the model, and is robust against removal techniques. We design our approach to work both in traditional and distributed learning settings such as Federated Learning, and demonstrate that it is effective even when a reduced number of bits is used for the model parameters. Finally, we implement a proof-of-concept self-extracting neural network malware using MaleficNet 2.0, demonstrating the practicality of the attack against a widely adopted machine learning framework. Our aim with this work is to raise awareness against these new, dangerous attacks both in the research community and industry, and we hope to encourage further research in mitigation techniques against such threats.