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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗
DOE OSTI2022

Protection and restoration of coastal habitats yield multiple benefits for urban residents as sea levels rise

Globally, rising seas threaten massive numbers of people and significant infrastructure. Adaptation strategies increasingly incorporate nature-based solutions. New science can illuminate where these solutions are appropriate in urban environments and what benefits they provide to people. Together with stakeholders in San Mateo County, California, USA, we co-developed nature-based solutions to support adaptation planning. We created six guiding principles to shape planning, summarized vulnerability to sea-level rise and opportunities for nature-based solutions, created three adaptation scenarios, and compared multiple benefits provided by each scenario. Adaptation scenarios that included investments in nature-based solutions deliver up to eight times the benefits of a traditionally engineered baseline as well as additional habitat for key species. The magnitude and distribution of benefits varied at subregional scales along the coastline. Our results demonstrate practical tools and engagement approaches to assessing the multiple benefits of nature-based solutions in an urban estuary that can be replicated in other regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evidence-based target setting informs blue carbon strategies for nationally determined contributions

The magnitude and pace of global climate change demand ambitious and effective implementation of nationally determined contributions (NDCs). Nature-based solutions present an efficient approach to achieving mitigation, adaptation and resilience goals. Yet few nations have quantified the diverse benefits of nature-based solutions to evaluate and select ecosystem targets for their NDCs. Here we report on Belize’s pursuit of innovative, evidence-based target setting by accounting for multiple benefits of blue carbon strategies. Through quantification of carbon storage and sequestration and optimization of co-benefits, we explore time-bound targets and prioritize locations for mangrove protection and restoration. We find increases in carbon benefits with larger mangrove investments, while fisheries, tourism and coastal risk-reduction co-benefits grow initially and then plateau. We identify locations, currently lacking protected status, where prioritizing blue carbon strategies would provide the greatest delivery of co-benefits to communities. These findings informed Belize’s updated NDCs to include an additional 12,000 ha of mangrove protection and 4,000 ha of mangrove restoration, respectively, by 2030. Our study serves as an example for the more than 150 other countries that have the opportunity to enhance greenhouse gas sequestration and climate adaptation by incorporating blue carbon strategies that provide multiple societal benefits into their NDCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework that builds on the deep deterministic policy gradient from demonstrations to identify an optimal restoration strategy. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. In conclusion, the proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Du, Yan↗
DOE OSTI2025

Adapting Grid Criticality for Data Centers

This presentation explores the evolving definition of “critical load” in the electric grid, emphasizing the growing importance of digital infrastructure—particularly data centers—in grid resilience, restoration, and modernization. As utilities increasingly rely on AI-driven analytics and software-defined control systems, data centers have shifted from passive electricity consumers to essential computational hubs that enable National Critical Functions (NCFs) and support real-time grid operations. The deck examines the scale and impact of digital loads, the need for grid modernization to manage rapid load growth, and the diverse computing paradigms required for AI deployment. It introduces a tiered taxonomy for classifying critical loads, highlights operational dependencies between the grid and digital infrastructure, and discusses policy implications for integrating data centers into emergency planning and restoration protocols. Through case studies and practical frameworks, the presentation provides actionable insights for utilities, regulators, and planners navigating the digital transformation of the power sector.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗
DOE OSTI2026

SDN-Based Smart Cyber Switching (SCS) for Cyber Restoration of a Digital Substation

In recent years, critical infrastructure and power grids have increasingly been targets of cyber-attacks, causing widespread and extended blackouts. Digital substations are particularly vulnerable to such cyber incursions, jeopardizing grid stability. This paper addresses these risks by proposing a cybersecurity framework that leverages software-defined networking (SDN) to bolster the resilience of substations based on the IEC- 61850 standard. The research introduces a strategy involving smart cyber switching (SCS) for mitigation and concurrent intelligent electronic device (CIED) for restoration, ensuring ongoing operational integrity and cybersecurity within a substation. The SCS framework improves the physical network’s behavior (i.e., leveraging commercial SDN capabilities) by incorporating an adaptive port controller (APC) module for dynamic port management and an intrusion detection system (IDS) to detect and counteract malicious IEC-61850-based sampled value (SV) and generic object-oriented system event (GOOSE) messages within the substation’s communication network. The framework’s effectiveness is validated through comprehensive simulations and a hardware-in-the-loop (HIL) testbed, demonstrating its ability to sustain substation operations during cyber-attacks and significantly improve the overall resilience of the power grid.

Liu, Chen-Ching (ORCID:0000000289417958)↗
DOE OSTI2026

Oxidative Deboronation of Boronic Acids by Hydrogen Peroxide in Planta Generates Borate for Cross-Linking of Rhamnogalacturonan II

Vascular plants require boron to cross-link the rhamnogalacturonan-II (RG-II) domain of pectin to form functional cell walls. Boronic acids, which form reversible esters with cis-diols like borate, have been proposed to influence RG-II cross-linking, though the mechanism remains unclear. We used suspension-cultured rose cells adapted to grow without boron to investigate the effect of boronic acids on RG-II dimerization. When grown with phenylboronic acid (PBA) as the sole boron source, nearly all RG-II was crosslinked, whereas methylboronic acid (MBA) only partially restored cross-linking. In contrast, in vitro assays showed that homogeneous RG-II monomers did not dimerize with alkyl or aryl boronic acids unless supplemented with hydrogen peroxide (H2O2), which oxidatively converts boronic acids to boric acid. Real-time NMR spectroscopy and density functional theory calculations provided insight into the reaction mechanism and energetics of oxidation respectively. Together, our data show that exogenous boronic acids are a source of boric acid for plants, and that the deboronation reaction generates aryl or alkyl alcohol byproducts that can undergo further chemical modification in planta. The fate and potential roles of these byproducts in planta remain to be determined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Repairable and Reconfigurable Structured Liquid Circuits

The advance of printed electronics is significantly bolstered by the development of liquid-state electronics that overcome the inherent limitations in flexibility and reconfigurability of solid-state electronics. By integrating the biocompatibility and conductivity of sulfonated polyaniline (S-PANI) and phytic acid (PA) with the reconfigurability of structured liquids, highly conductive all-liquid threads are developed. The dense packing and overlap of PA/S-PANI complexes at an oil/water interface promotes in-plane electron transport, and standard four-point probe measurements of PA/S-PANI interfacial assemblies demonstrate enhanced electrical properties. Notably, the rapid jetting of the ink phase into the matrix phase allows for liquid threads to be printed, enabling the fabrication of large-scale, conductive pathways between two electrodes and liquid circuits. Upon mechanical cleavage of the liquid wires, circuits can be broken, but will easily self-repair using an electric field, making this motif useful in the design of switches as well as restoring conductive pathways in series or in parallel. In conclusion, the demonstrated flexibility and reconfigurability these PA/S-PANI wires possess hold significant promise for their practical use in the design of flexible and adaptive bioelectronics that can be repaired on demand, signifying a transformative step in the evolution of liquid electronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Catch bond kinetics are instrumental to cohesion of fire ant rafts under load

Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants ( S. invicta ). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35 % strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that—even upon reinstatement of initial densities—ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants’ status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.

Science & Technology - Other Topics↗
DOE OSTI2026

Single nucleotide variants drive evolutionary phage-host arms race in anaerobic carbon dioxide-converting microbiome

Microbial bioconversions are shaped by environmental perturbations and the adaptation of resident microbiomes. Prokaryotes coexist with bacteriophages, yet their coevolutionary trajectories remain underexplored. Here, we investigate the effects of a cultivation vessel leak on an anaerobic consortium performing carbon dioxide reduction. Using time-series shotgun metagenomic sequencing, we reconstruct microbial and viral genomes to track community shifts. We further apply single-nucleotide variant profiling and CRISPR array analysis to monitor viral microdiversity and host defense mechanisms. After bioaugmentation restores bioconversion efficiency, the consortium undergoes pronounced restructuring, with new dominant taxa emerging from the rare biosphere. We identify patterns consistent with phage predation selectively removing certain species, while others exhibit resilience to infection. This shift aligns with a widespread viral outbreak and a transient increased frequency of single nucleotide variants in bacterial CRISPR–Cas defense genes. Expansion of CRISPR spacers further supports that CRISPR-mediated processes influence microbial resilience. Concurrently, phages infecting resilient hosts exhibited adaptive evolution, marked by high genetic heterogeneity. Selective pressure varies across their genomes, targeting infectivity genes and protospacer-adjacent motifs. These findings highlight a dynamic evolutionary arms race driven by the selection of beneficial genetic variants, providing a mechanistic framework for multi-omics investigations, and informing biotechnological applications, including phage-based microbiome manipulation.

Ghiotto, G↗
DOE OSTI2023

Symmetry-projected cluster mean-field theory applied to spin systems

We introduce S z spin-projection based on cluster mean-field theory and apply it to the ground state of strongly correlated spin systems. In cluster mean-fields, the ground state wavefunction is written as a factorized tensor product of optimized cluster states. In previous work, we have focused on unrestricted cluster mean-field, where each cluster is S z symmetry adapted. We here remove this restriction by introducing a generalized cluster mean-field (GcMF) theory, where each cluster is allowed to access all S z sectors, breaking S z symmetry. In addition, a projection scheme is used to restore global S z , which gives rise to the S z spin-projected generalized cluster mean-field (S z GcMF). Both of these extensions contribute to accounting for inter-cluster correlations. We benchmark these methods on the 1D, quasi-2D, and 2D J 1 – J 2 and XXZ Heisenberg models. Furthermore, our results indicate that the new methods (GcMF and S z GcMF) provide a qualitative and semi-quantitative description of the Heisenberg lattices in the regimes considered, suggesting them as useful references for further inter-cluster correlations, which are discussed in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Evaluating Basin-Scale Forest Adaptation Scenarios: Wildfire, Streamflow, Biomass, and Economic Recovery Synergies and Trade-Offs

Active forest management is applied in many parts of the western United States to reduce wildfire severity, mitigate vulnerability to drought and bark beetle mortality, and more recently, to increase snow retention and late-season streamflow. A rapidly warming climate accelerates the need for these restorative treatments, but the treatment priority among forest patches varies considerably. We simulated four treatment scenarios across the 3,450 km 2 Wenatchee River basin in eastern Washington, United States. We used a decision support tool (DST) to assess trade-offs and synergies within and among treatments on wildfire risk and smoke emissions, water yield and snow retention, biomass production, and economic return. Treatment scenarios emphasized prescribed burning ( BurnOnly ), biomass production ( MaxBiomass ), gap-based thinning to optimize water yield ( IdealWater ), and a principle-based restoration scenario ( RA1 ). Fire hazard, smoke emissions, and biomass production metrics were evaluated across scenarios using the Forest Vegetation Simulator, and water yields were modeled using the Distributed Hydrology Soil Vegetation Model. Simulations were summarized to both patch- (10 1 –10 2 ha) and subwatershed- (10 3 –10 4 ha) scales, and treatment effects were evaluated against an untreated baseline landscape. We used logic models to rank effect sizes by scenario across metrics along a continuum between −1 (no or weak effect) to +1 (large effect). All treatments produced benefits across one or more ecosystem services and led to synergistic benefits to water yield and wildfire hazard reduction. Tradeoffs among resource benefits were clear in wilderness where reliance on prescribed burning without mechanical treatment increased costs and eliminated the potential for biomass recovery. The BurnOnly scenario improved fire risk metrics and streamflow, but effect sizes were lower compared to other treatments. IdealWater showed the strongest benefits overall, demonstrating the ability to capture multiple resource benefits through spatially explicit thinning. Our study provides a framework for integrating strategic and tactical models that evaluate tradeoffs and synergies gained through varied management approaches. We demonstrate the utility of decision support modeling to enhance management synergies across large landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Riparian vegetation composition and diversity shows resilience following cessation of livestock grazing in northeastern Oregon, USA

Riparian ecosystem restoration has been accomplished through exclusion of livestock using corridor fencing along hundreds of kilometers of streams in the western United States, for the benefit of riparian-obligate wildlife and endangered fishes. Yet, there are limited scientific studies that have evaluated more than short-term shifts in vegetation composition and diversity at a single location or handful of locations following grazing. We sampled riparian vegetation composition along 11-paired grazed and ungrazed (exclosed) stream reaches in northeastern Oregon. Exclosure ages ranged from 2 to >30 years and grazing treatments varied from light grazing every one out of three years to heavy season-long grazing. Species richness and diversity was higher in the ungrazed reaches (p = 0.002). The abundance of native sedges ( Carex spp.) and broad-leaved forbs were also significantly (p ≤ 0.05) greater in ungrazed areas. In contrast, exotic species adapted to grazing such as Poa pratensis and Trifolium repens were more abundant in grazed stream reaches. The prevalence of hydrophytic species significantly increased (p ≤ 0.01) in ungrazed reaches, (based on wetland species indicator scores), indicating that wetland-dominated communities within the ungrazed stream reaches were replacing ones adapted to drier environments. The increased abundance of facultative and wetland-obligate species in ungrazed reaches compared to grazed reaches suggests that livestock grazing exacerbates those climate change effects also leading to warmer and drier conditions. Further, riparian-obligate shrub cover along the streambank was higher in 7 of 8 exclosures that were older than 5 years (p = 0.05). As a restoration approach, the inherent resilience of riparian ecosystems exhibited in ungrazed riparian zones suggest positive feedbacks to other beneficial ecosystem processes such as increased species and habitat diversity, increased carbon sequestration, enhanced allochthonous inputs and greater sediment retention, that would affect the aquatic and terrestrial biota, water quality, and stream morphology.

Kauffman, J. Boone (ORCID:0000000290202815)↗
DOE OSTI2026

Adaptive_Evolution_2026

Thermoanaerobacterium saccharolyticum, an anaerobic and thermophilic bacterium capable of metabolizing sugar monomers and soluble oligomers into ethanol, has been proposed for use in consolidated bioprocessing in coculture with compatible cellulolytic bacteria such asClostridium thermocellum. Although the mixed acid fermentation of both of these strains has been engineered to produce ethanol as the major fermentation product, the maximum titer produced thus far byC. thermocellum, about 3% (w/v) ethanol, is half that produced byT. saccharolyticum. There is thus motivation to understand the mechanistic basis of the robust ethanol pathway inT. saccharolyticumso that key features can be recapitulated inC. thermocellumand other organisms.Previously, we characterized theindividualrole of the main genes responsible for electron transfer in the ethanol production ofT. saccharolyticum. However, the consequences of thecombinedloss of function of all these genes have not been investigated, nor has the way in which fermentative metabolism adapts to such constraints. In this work, we combined knockouts of ferredoxin nicotinamide oxidoreductase (fnor) genes (nfnAandnfnB) and hydrogenase genes (hydAandhfsD) and studied their effects on fermentation and grow. We showed that these genetic modifications together impair growth and decrease electron transfer from reduced ferredoxin, thereby redirecting flux from the pyruvate ferredoxin oxidoreductase enzyme to the pyruvate formate lyase enzyme. We also performed adaptive evolution of these mutants to rescue their growth, and most notably, we observed a single nucleotide variation in the alcohol dehydrogenaseadhAgene. Through molecular dynamics simulations and enzymatic assays, we determined that this point mutation causes a structural change that impairs the AdhA specificity for the NADPH cofactor and increases NADH-linked activity to restore redox balance. These findings consolidate our understanding of the functioning of electron transfer pathways in this organism.

alcohol dehydrogenase↗
DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗