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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Mars Planetary Network for Human Exploration Era – Potential Challenges and Solutions

During 2016-2017, a study was conducted under the sponsorship of the NASA’s Space Communications and Navigation (SCaN) Program to investigate the deep space communications capacity taking into account the needs of all the present and envisioned future missions toward 2030s. It was soon recognized that planning for human exploration to Mars would impose certain unprecedent challenges, both fiscal and technical, to the current space communications paradigm. Targeting the assumed missions concepts, i.e., Crewed Mission to Phobos (CMTP) and Mars Short Stay Mission (MSSM), a Mars Planetary Network for the human exploration era has been formulated. The activity modeling and network traffic simulation/modeling we performed gave some insight into the technical challenges in space communications for the envisioned human Mars exploration era. Chief among the potential challenges are: (1) the high demand on the deep space network (DSN) assets for achieving the high-rate links, both return and forward, from Mars farthest/farther distance (up to 2.67 AU); (2) the need for resilient, persistent communication coverage for crewed vehicles, on surface and in orbits; (3) the significant period of outage for the Mars-Earth link due to superior solar conjunction; (4) the need for on-demand, simultaneous access to the proximity link by multiple vehicles and astronauts in the exploration zone; (5) the capability of determining precise, real-time, positions of surface vehicles and astronauts by the deep space habitat and/or other tele-operations entities. Solution space to each of the above challenges has been explored and analyzed in the context of the individual problem domain and, more importantly, in conjunction with that for the other challenges. This has led to an end-to-end definition of a Mars Planetary Network that would feature: (1) the fusion of deep space Ka-band and optical communications for achieving Mars-Earth high-rate links taking advantage of the optical/RF hybrid 8m/34m antennas in DSN; (2) the integrated application of the Multiple Spacecraft Per Antenna (MSPA) technique, for return link data acquisition, and the Multiple Uplink Per Antenna (MUPA) technique, for forward link data trasnmission, to reduce the number of 34m beam-wave guide (BWG) antennas needed for the era; (3) the integration of three, arrayed, 34m beam-wave guide (BWG) antennas, to provide a high G/T aperture, with the MSPA/MUPA techniques, and a dual “trunk link”approach to cut down the needed G/T -- hence, reducing the number of 34m antennas, relative to that in the single trunk approach by 50%; (4) the deployment of two areostationary/areosynchronous Mars relay orbiters; one of them could also function as (or be served by) a notional Deep Space Habitat (DSH); (5) the opportunistic deployment of a science orbiter in a Pioneer-6 type orbit, equidistant Mars/Earth, that could also serve as an intermediary relay during the Mars superior solar conjunction perod; (6) the existence of the multi-function Mars proximity link that provides the demand-assigned, multiple access (DAMA) capability; and (7) the provision of tracking observables, by leveraging on the planned Mars orbiting and surface infrastructure, to enable the in-situ navigation for surface and orbiting vehicles. This paper provides the description of the proposed Mars Planetary Network in the human exploration era, the trade-off analysis for the various alternative architectures, and the optimal solutions to the key challenges in defining this end-to-end network.

Cheung, Kar-Ming↗
NASA NTRSDate not supplied

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.

CNN↗
NASA NTRS2021

Acoustic Sensor Network for Planetary Exploration

This paper investigates the concept of an acoustic sensor network that can monitor a variety of geophysical processes occurring on other planetary bodies. In many cases sound is naturally omnidirectional and travels at known speeds which depend on the composition and density of the atmosphere. The differences in the time of flight of signals received by a distributed microphone network can be used to locate the source of the sound. We suggest this property is ideal for mobile planetary robots and can be used to expand the exploration envelope considerably by directing camera pointing or rover path planning thus extending beyond line-of-sight exploration. Acoustic signatures have been used in a variety of fields (e.g., sonar, heavy machinery) to identify and catalog sounds associated with a specific vessels and malfunctioning machinery. Our ears have cataloged hundreds of sounds and we continuously use these sounds both consciously and subconsciously to extract information about our surroundings. This paper investigates the use acoustic measurements on other planetary bodies that could be used to characterize specific environmental parameters such as rain droplet size, wind speed, thunder, or dust devil vortex diameter. The paper identified other important sound sources that are thought to occur on other bodies in our solar system include; booming or singing dunes, waves, rivers, streams, fluidfalls, geysers, hurricanes, tornados, ice flow, volcanoes, planetary quakes, avalanche, rock slides and ice cracking. In addition, this paper focused on issues associated with the development of appropriate sensors for the network including the specification of the sensitivity, frequency response, and directional response of each of the microphones in the network in order to aid in localization of sound sources. We also presented our initial development of the transducers for potential mission targets including Mars and Titan and investigated the use of signal processing techniques including windowing, time frequency plots and correlation techniques to resolve phase differences between sensors in the network to aid in localization. We also identified additional benefits of these sensor networks in that they could used as engineering sensors to diagnose mechanical malfunctions on a rover or lander actuators or mechanisms. We also noted that they could also enhance public outreach by adding sound to videos.

Malaska, Mike↗
NASA NTRS2021

A Dedicated Relay Network to Enable the Future of Mars Exploration

A highly successful international collaboration, the “Mars Relay Network” (MRN) leverages the combined NASA and ESA orbiter capabilities to transfer data to and from Mars surface missions. The MRN has admirably flight-demonstrated the benefits of a relay network and validated how international protocol standards may be used to ensure interoperability. However, principally designed for science missions, these orbiters addressed relay requirements as a secondary function, which introduced limitations to what can be achieved with the network. Next-decade missions are expected to have significantly greater communication needs than can be accommodated by the aging MRN. This paper reports the results of a broad study that evaluated the potential of a next-generation relay network, referencing the current MRN as a benchmark. A wide variety of orbital altitudes, surface latitudes, and mission scenarios were evaluated around a specific set of assumptions regarding the telecommunications payloads included. The study outlined how current day technologies could be applied to greatly enhance the data throughput to and from Mars on behalf of future science and reconnaissance missions. The instantiation of such a network would be enabling for a variety of missions and mission classes that have been heretofore unachievable, including both large and small orbiters, and landed vehicles representing new mission types (i.e. climbers, diggers, drones, etc.), and further argues that such a network would be instrumental in advancing human exploration interests at Mars.

Davis, Richard M.↗
NASA NTRS2022

Quantifying Spatial Drought Propagation Potential in North America Using Complex Network Theory

Droughts have a dominant three-dimensional (3-D) spatiotemporal structure typically spanning hundreds of kilometers and often lasting for months to years. Here, we introduced a novel framework to explore the 3-D structure of the evolution of droughts based on network theory concepts. The proposed framework is applied to identify critical source regions responsible for large-scale drought onsets during 1901–2014 for the North American continent using the Standardized Precipitation Evaporation Index (SPEI). We built a spatial network connecting the drought onset timings for the North American continent. Using a spatially weighted network partitioning algorithm, the whole continent is then classified into regional spatial drought networks (RSN), where droughts are more likely to propagate within these regional systems. Finally, a customized network metric was applied to identify locations (source regions) where the drought onsets further propagate to other areas within the regional spatial network. Our results indicated that the West coast, Texas coastal region, and Southeastern Arkansas as major source regions through which atmospheric drought propagates to Western, South Central, and Eastern North America. The formation of drought source regions are due to presence of high pressure ridges and anomalous wind patterns. Furthermore, our results indicate that the drought propagation from these source regions may be due to inadequate moisture transport. The proposed framework can help to develop an early warning detection system for droughts and other spatially extensive extreme events such as heatwaves and floods.

Goutam Konapala↗
NASA NTRSDate not supplied

INSPiRE – An Approach to Mission Quality Management using Network Slicing for Space Applications

Managing traffic between the Earth-Moon and Earth-Mars is a complex process requiring significant investment in resources and expertise at NASA. INSPiRE improves the performance of space networks by enabling a dynamic re-configuration process that works for any mixed topology over a heterogeneous and multi-vendor network. To achieve the desired functionality, INSPiRE incorporates a set of algorithms, machine learning processes, and policy inference to handle unpredictable, disruptive events. INSPiRE draws parallels from the current notion of the 3GPP (5G and beyond) Network Slicing approach, where the same physical network divides into several virtual networks, and for each of these virtual networks, there is a guaranteed Quality of Service for the missions that they serve.

cognitive communications↗
DOE OSTI2025

Hierarchical Resilience Planning for Networked Microgrids: A Case Study of Puerto Rico

Microgrids can be designed to enhance the energy resilience of communities and critical infrastructures, such as hospitals, data centers, and communication networks, which are vulnerable to frequent weather-related disruption. Coordinating multiple microgrids in a network can leverage the geographical diversity of load and generation resources while enabling resilient and cost-effective planning of the distribution system. Designing a networked microgrid is complex, involving intricate technical assessment, cost-benefit analysis, site-specific requirements, and the evaluation of existing resources. Therefore, this paper proposes a hierarchical resilience planning framework and performs an extensive techno-economic analysis for the design of a networked microgrid. Hierarchical resilience planning involves technology sizing at an individual community level to meet the critical load and satisfy resilience criteria, and resource optimization at networked microgrid level to provide a higher level of resilience and energy adequacy. A real-world case of Puerto Rico's cooperative microgrid “Microrred de la Montaña” is investigated considering localized electricity tariffs, site-specific demand profiles, solar generation, and existing hydro resources. Multiple optimization scenarios are developed based on the resiliency requirement to estimate the capacity of solar photovoltaic and battery energy storage (BES) to be installed at each substation. The results provide the optimal sizing for individual community and networked microgrid to withstand 1day and 3-day outages along with the criteria for critical load.

13 - HYDRO ENERGY↗
DOE OSTI2024

Sequence-Based Anomaly Detection in Critical Infrastructure Networks

United States critical infrastructure faces new cyber threats from adversarial nation-state actors in the form of malware-free attacks. Traditional cybersecurity techniques use rules-based methods to identify indicators of compromise on networks, often missing these sophisticated attacks. Our approach leverages multiple state of the art machine learning models in a pipeline to identify abnormal network events through sequential analysis. We combine both device and packet-level information into individual events to characterize anomalous network actions. The model is trained and tested on real network traffic from the Idaho National Lab High Performance Computing (HPC) with greater than 98% precision. It is capable of flagging malicious tactics used by adversaries in malware-free attacks, severe changes to the network, and abnormal user activity by network devices.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses

Graph Convolutional Neural Networks (GCNNs) have emerged as powerful tools for analyzing materials. In this study, we employ GCNNs to examine structural characteristics of CuZr metallic glasses (MGs) and identify their states. We use molecular dynamics to simulate the quenching process of CuZr, using cooling rates ranging from 10 9 to 10 15 K/s, to produce six unique glassy states. For each state, we create a dataset comprising 1,800 distinct samples. We evaluate the effectiveness of various GCNNs, including Graph Attention Neural Network (GANN), Graph Sample and AggreGatE (GraphSAGE), Graph Isomorphism Network (GIN), and Relational Graph Convolutional Neural Network (RGCN). GANN and GraphSAGE demonstrate comparable performance, achieving an overall accuracy of 81% in classifying the MG states. Furthermore, these results underscore the potential of GCNNs to detect subtle structural variances in disordered materials and point to broader application of deep learning in the analysis of MGs and other amorphous substances.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Simulation Modeling and Performance Evaluation of Space Networks

In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions

network protocols↗
NASA NTRSDate not supplied

Introducing Tropical Geometric Approaches to Delay Tolerant Networking Optimization

Delay Tolerant Networking (DTN) is the standard approach to the networking of space systems with the goal of supporting the Solar System Internet (SSI). Current space networks have a small scale and often depend on rigorously scheduled (pre-determined) contact opportunities; this manual approach inhibits scalability. The goal of this paper is to recast these scheduling problems in order to apply the optimization machinery of tropical geometry. Contact opportunities in space are dependent on such factors as orbital mechanics and asset availability, which induce time-varying connectivity; indeed, end-to-end connectivity might never occur. Routing optimization within this structure is classically difficult and typically utilizes Dijkstra's algorithm as applied to contact graphs. Alternatively, we follow the successes of tropical geometry in train schedule optimization, job assignments, and even traditional networking, by extending this approach to this more general (i.e. disconnected) problem space. These successes imply tropical geometry provides a useful framework in the context of DTNs, starting with applications to queuing theory and long-haul links. Recently, tropical geometry has been applied to parametric path optimization on graphs with variable edge weights. In this work, we extend these advances to account for the problem of routing in a space network, and find that tropical geometry is well-suited to the challenges offered by this new setting, including contact schedules featuring probabilities. Our approach leverages the combinatorial nature of the problem to give feasible shortest path trees in the presence of variable channel conditions and latency, evolving topologies, and uncertainty inherent in space routing. We discuss our tropical approach to DTN for two Python implementations, a Verilog Tropical ALU implementation, tropical frameworks for other parametric graph problems, and solution stability. Lastly, a program for future work is included to illuminate the path ahead.

Delay Tolerant Networking↗
NASA NTRS2024

Short–Period Variables in TESS Full–Frame Image Light Curves Identified via Convolutional Neural Networks

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ∼85% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data set, we aim to provide an approach that is computationally efficient, produces accurate predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short-period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in ∼5 ms on a single GPU, enabling large-scale archival searches. We present a collection of 14,156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of close-orbit main-sequence binaries and another of δ Scuti stars. Our neural network model and related code are additionally provided as open-source code for public use and extension.

Convolutional neural networks↗
DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗
DOE OSTI2026

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗