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This article for the Domestic Preparedness Journal introduces the Emergency Management of Tomorrow Research Program and its role exploring artificial intelligence for emergency management and emergency operation centers in the future.
Purpose of Review: Various imaging techniques used in stroke evaluation have complementary, evolving roles. This article reviews recent updates in emergent stroke neuroimaging. Recent Findings: Multiple recent randomized trials showing benefit of endovascular therapy in strokes with confirmed large-vessel occlusion emphasize need for vessel imaging, typically CTA, in addition to unenhanced head CT. In addition, because recent trials published in 2018 showed efficacy of mechanical thrombectomy at up to 24 h from symptom onset in stroke patients with favorable ischemic core and/or penumbra assessments, imaging techniques to define viable and infarcted tissue, such as DWI or CT/MR perfusion imaging, have an increasing role in emergent stroke assessment. Other studies contributing to new AHA/ASA stroke guidelines are also discussed. Summary: Neuroimaging remains crucial to emergent stroke evaluation, with CTA, MRI, and perfusion studies playing an increasing role in identifying patients likely to benefit from treatment.
We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.
Health physics is an important discipline with regard to understanding the effects of radiation on human health; however, there are major differences between health physics for research or occupational safety and health physics during a large-scale radiological emergency. The deployment of a U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) monitoring and assessment team to Japan in the wake of the March 2011 accident at Fukushima Daiichi Nuclear Power Plant yielded a wealth of lessons on these difference. Critical teams (CMOC (Consequence Management Outside the Continental U.S.) and CMHT (Consequence Management Home Team) ) worked together to collect, compile, review, and analyze radiological data from Japan to support the response needs of and answer questions from the Government of Japan, the U.S. military in Japan, the U.S. Embassy and U.S. citizens in Japan, and U.S. citizens in America. This paper addresses the unique challenges presented to the health physicist or analyst of radiological data in a large-scale emergency. A key lesson learned was that public perception and the availability of technology with social media requires a diligent effort to keep the public informed of the science behind the decisions in a manner that is meaningful to them.
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Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.
In 2013 June, the two scientific instruments on board the second Sunrise mission witnessed, in detail, a small-scale magnetic flux emergence event as part of the birth of an active region. The Imaging Magnetograph Experiment (IMaX) recorded two small (∼5{sup ′′}) emerging flux patches in the polarized filtergrams of a photospheric Fe i spectral line. Meanwhile, the Sunrise Filter Imager (SuFI) captured the highly dynamic chromospheric response to the magnetic fields pushing their way through the lower solar atmosphere. The serendipitous capture of this event offers a closer look at the inner workings of active region emergence sites. In particular, it reveals in meticulous detail how the rising magnetic fields interact with the granulation as they push through the Sun’s surface, dragging photospheric plasma in their upward travel. The plasma that is burdening the rising field slides along the field lines, creating fast downflowing channels at the footpoints. The weight of this material anchors this field to the surface at semi-regular spatial intervals, shaping it in an undulatory fashion. Finally, magnetic reconnection enables the field to release itself from its photospheric anchors, allowing it to continue its voyage up to higher layers. This process releases energy that lights up the arch-filament systems and heats the surrounding chromosphere.
The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) is partnering with Pacific Northwest National Laboratory (PNNL) to execute the Emergency Management (EM) of Tomorrow Research (EMOTR) program to identify current EM research, elicit capability needs from EM practitioners, and identify where technology, such as artificial intelligence (AI), may benefit the future of EM and emergency operations centers. This report details the methodology, analysis, and insights of interviews and focus groups conducted as part of the task to elicit stakeholder input.
Abstract Methane flux from freshwater mineral‐soil (FWMS) wetlands and its variability among sites is largely modulated by plant‐mediated transport. However, plant‐mediated transport processes are rarely resolved in land surface models and are poorly parametrized for plants commonly found in FWMS wetlands. Here, relationships between methane flux and CO 2 uptake, as well as plant conductance of methane were evaluated for three plant species and two characteristic functional types: emergent (narrow‐leaved cattail) and floating‐leaved (American lotus and water lily). We found significant but contrasting correlations between methane flux and CO 2 uptake in cattails ( r 2 = 0.51, slope = −0.16, during morning) and water lily ( r 2 = 0.32, slope = 0.064, after midday). This relationship was not significant in American lotus, showing that stomata regulation of methane fluxes is species‐specific and not generalizable across the floating‐leaved plant functional type. Conductance of methane per leaf area showed distinct seasonal dynamics across species. Conductance was similar among the floating‐leaved species (6.2 × 10 −3 m d −1 in lotus and 7.2 × 10 −3 m d −1 in water lily) and higher than conductance in the emergent species (2.7 × 10 −3 m d −1 ). Our results provide direct observations of plant conductance rates and identify the vegetation parameters (leaf area, stomatal conductance) that modify them. Our results further suggest that models of methane emissions from FWMS should parameterize plant‐mediated transport in different plant functional types, scaled by leaf area and with variable seasonal phenological dynamics, and consider possible species‐specific mechanisms that control methane transport through plants.
Pressure has an essential role in the production and control of superconductivity in iron-based superconductors. Substitution of a large cation by a smaller rare-earth ion to simulate the pressure effect has raised the superconducting transition temperature T{sub c} to a record high of 55 K in these materials. In the same way as T{sub c} exhibits a bell-shaped curve of dependence on chemical doping, pressure-tuned T{sub c} typically drops monotonically after passing the optimal pressure. Here we report that in the superconducting iron chalcogenides, a second superconducting phase suddenly re-emerges above 11.5 GPa, after the T{sub c} drops from the first maximum of 32 K at 1 GPa. The T{sub c} of the re-emerging superconducting phase is considerably higher than the first maximum, reaching 48.0-48.7 K for Tl{sub 0.6}Rb{sub 0.4}Fe{sub 1.67}Se{sub 2}, K{sub 0.8}Fe{sub 1.7}Se{sub 2} and K{sub 0.8}Fe{sub 1.78}Se{sub 2}.
Abstract Interactive microbial communities are ubiquitous, influencing biogeochemical cycles and host health. One widespread interaction is nutrient exchange, or cross-feeding, wherein metabolites are transferred between microbes. Some cross-fed metabolites, such as vitamins, amino acids, and ammonium (NH4+), are communally valuable and impose a cost on the producer. The mechanisms that enforce cross-feeding of communally valuable metabolites are not fully understood. Previously we engineered a cross-feeding coculture between N2-fixing Rhodopseudomonas palustris and fermentative Escherichia coli. Engineered R. palustris excretes essential nitrogen as NH4+ to E. coli, while E. coli excretes essential carbon as fermentation products to R. palustris. Here, we sought to determine whether a reciprocal cross-feeding relationship would evolve spontaneously in cocultures with wild-type R. palustris, which is not known to excrete NH4+. Indeed, we observed the emergence of NH4+ cross-feeding, but driven by adaptation of E. coli alone. A missense mutation in E. coli NtrC, a regulator of nitrogen scavenging, resulted in constitutive activation of an NH4+ transporter. This activity likely allowed E. coli to subsist on the small amount of leaked NH4+ and better reciprocate through elevated excretion of fermentation products from a larger E. coli population. Our results indicate that enhanced nutrient uptake by recipients, rather than increased excretion by producers, is an underappreciated yet possibly prevalent mechanism by which cross-feeding can emerge.
We study the quantum evolution in dimension three of a system composed by a test particle interacting with an environment made of N harmonic oscillators. At time zero the test particle is described by a spherical wave, i.e., a highly correlated continuous superposition of states with well localized position and momentum, and the oscillators are in the ground state. Furthermore, we assume that the positions of the oscillators are not collinear with the center of the spherical wave. Under suitable assumptions on the physical parameters characterizing the model, we give an asymptotic expression of the solution of the Schrödinger equation of the system with an explicit control of the error. The result shows that the approximate expression of the wave function is the sum of two terms, orthogonal in L{sup 2}(R{sup 3(N+1)}) and describing rather different situations. In the first one, all the oscillators remain in their ground state and the test particle is described by the free evolution of a slightly deformed spherical wave. The second one consists of a sum of N terms where in each term there is only one excited oscillator and the test particle is correspondingly described by the free evolution of a wave packet, well concentrated in position and momentum. Moreover, the wave packet emerges from the excited oscillator with an average momentum parallel to the line joining the oscillator with the center of the initial spherical wave. Such wave packet represents a semiclassical state for the test particle, propagating along the corresponding classical trajectory. The main result of our analysis is to show how such a semiclassical state can be produced, starting from the original spherical wave, as a result of the interaction with the environment.
The use of plant materials to generate renewable biofuels and other high-value chemicals is the sustainable and preferable option, but will require considerable improvements to increase the rate and efficiency of lignocellulose depolymerization. This review highlights novel and emergent technologies that are being developed and deployed to characterize the process of lignocellulose degradation. The review will also illustrate how microbial communities deconstruct and metabolize lignocellulose by identifying the necessary genes and enzyme activities along with the reaction products. These technologies include multi-omic measurements, cell sorting and isolation, nuclear magnetic resonance spectroscopy (NMR), activity-based protein profiling, and direct measurement of enzyme activity. The recalcitrant nature of lignocellulose necessitates the need to characterize the methods microbes employ to deconstruct lignocellulose to inform new strategies on how to greatly improve biofuel conversion processes. New technologies are yielding important insights into microbial functions and strategies employed to degrade lignocellulose, providing a mechanistic blueprint to advance biofuel production.
Gravity solutions dual to d-dimensional field theories at finite charge density have a near-horizon region, which is AdS{sub 2}xR{sup d-1}. The scale invariance of the AdS{sub 2} region implies that at low energies the dual field theory exhibits emergent quantum critical behavior controlled by a (0+1)-dimensional conformal field theories (CFT). This interpretation sheds light on recently-discovered holographic descriptions of Fermi surfaces, allowing an analytic understanding of their low-energy excitations. For example, the scaling behavior near the Fermi surfaces is determined by conformal dimensions in the emergent IR CFT. In particular, when the operator is marginal in the IR CFT, the corresponding spectral function is precisely of the ''marginal Fermi liquid'' form, postulated to describe the optimally doped cuprates.
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The prediction of high T c superconductivity in layers of LaNiO 3 through orbital engineering has led to extensive research efforts over the last fifteen years. During this period, a plethora of thin films and heterostructures based rare-earth nickelate family with perovskite structure has been synthesized and explored. Here, in this short perspective, we briefly review the complexity of bulk RENiO 3 , spotlighting several recent findings of emergent phenomena in heterostructures containing the interface between RENiO 3 and another transition metal oxide. Finally, we outline potentially interesting future directions linked to time-domain dynamics to harness new Mott and topological phases in artificial structures of RENiO 3 .
DEPRECATED This repository was archived by the owner on Jun 30, 2026. It is now read-only. EMeRGE (Emerging technologies Management and Risk evaluation on distribution Grids Evolution) is a collection of mini-tools to help users develop openDSS feeder model from GIS (.shp) file and perform risk analysis at various PV scenarios and visulize results in an interactive dashboard made using Dash.
Electrical vehicles (EVs) are an attractive option for moving towards a CO 2 neutral transportation sector, but in order for widespread commercial use of EVs, the cost of electrical energy storage (i.e., batteries) must be reduced and the energy storage capacity must be increased. New, higher performing but Earth abundant electrodes are needed to accomplish this goal. To aid the development of these materials, in situ characterization to understand battery operation and failure is essential. Since electrodes are inherently heterogeneous, with a range of relevant length scales, imaging is a necessary component of the suite of characterization methods. In this Feature Article, the rapidly growing and developing field of X‐ray based microscopy (XM) techniques is described and reviewed focusing on in situ and operando adaptations. Further, in situ transmission electron microscopy (TEM) is briefly discussed in this context and its complement to XM is emphasized. Finally, a perspective is given on some emerging X‐ray based imaging approaches for energy storage materials.