Publications

Abstract In this study, we propose a new technique for mapping the spatial heterogeneity in gas exchange around flux towers using flux footprint modeling and focusing on detecting hot spots of methane (CH 4 ) flux. In the first part of the study, we used a CH 4 release experiment to evaluate three common flux footprint models: the Hsieh model (Hsieh et al., 2000), the Kljun model (Kljun et al., 2015), and the K & M model (Kormann and Meixner, 2001), finding that the K & M model was the most accurate under these conditions. In the second part of the study, we introduce the Footprint‐Weighted Flux Map, a new technique to map spatial heterogeneity in fluxes. Using artificial CH 4 release experiments, natural tracer approaches and flux chambers we mapped the spatial flux heterogeneity, and detected and validated a hot spot of CH 4 flux in a oligohaline restored marsh. Through chamber measurements during the months of April and May, we found that fluxes at the hot spot were on average as high as 6589 ± 7889 nmol m −2 s −1 whereas background flux from the open water were on average 15.2 ± 7.5 nmol m −2 s −1 . This study provides a novel tool to evaluate the spatial heterogeneity of fluxes around eddy‐covariance towers and creates important insights for the interpretation of hot spots of CH 4 flux, paving the way for future studies aiming to understand subsurface biogeochemical processes and the microbiological conditions that lead to the occurrence of hot spots and hot moments of CH 4 flux.

This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2021 (FY 2021). In FY 2021, the DOE VTO battery R&D funding was approximately $115 million. Its R&D focus was on the development of high-energy batteries for EVs as well as very high-power devices for hybrid vehicles. The electrochemical energy storage roadmap (which can be found at the EERE Roadmap web page2) describes ongoing and planned efforts to develop electrochemical storage technologies for EVs. To advance battery technology, which can in turn improve market penetration of PEVs, the program investigates various battery chemistries to overcome specific technical barriers, e.g., battery cost, performance, life (both the calendar life and the cycle life), its tolerance to abusive conditions, and its recyclability/sustainability. VTO R&D has had considerable success, lowering the cost of EV battery packs to $185/kWh in 2019 (representing more than 80% reduction since 2008) yet even further cost reduction is necessary for EVs to achieve head-to-head cost competitiveness with ICEs (without Federal subsidies). In addition, today’s batteries also need improvements in such areas as their ability to accept charging at a high rate, referred to as extreme fast charging (XFC) (15 minute charge) – to provide a “refueling” convenience similar to ICEs, and the ability to operate adequately at low temperatures. Research into “next-gen lithium-ion” batteries which would provide such functionalities is one of the R&D focus areas. VTO is funding research on both “next gen” chemistries (which employ an alloy anode and/or a high voltage cathode) and beyond lithium-ion (BLI) chemistries (which can, for example, employ a lithium metal anode).

<p>Supplementary Table 3. The Adjusted Associations of Scaled Body Composition Measurements (Per SD Increase) with Reduced RDI and Relative Changes (%) in the Number of Moderate and Severe Adverse Events</p>

Understanding how the strengths of species interactions are distributed among species is critical for developing predictive models of natural food webs as well as for developing management and conservation strategies. Recently a number of ecologists have attempted to clarify the concepts of "strong-" and "weak-interactors" in a community, and to derive techniques for quantifying interaction strengths in the field, using metrics that are consistent, comparable, and of relevance to theoreticians. In this paper, we examine potential biases in different empirical approaches to quantifying variation in interaction strengths within and among natural communities. Using both simulated and published data, we explore the behavior of four commonly used or recently proposed empirical measures of the strength of consumer–prey interactions. The type of index used, the experimental protocol, and the underlying model of predator–prey interaction all strongly influence one's perception of both (1) the distribution of interaction strengths among species (e.g., presence of "keystone" species), and (2) the specific identity of the interactions that appear to be most important. Raw treatment differences tend to emphasize effects on very abundant prey, while the three proportional indices tend to emphasize effects on extremely rare prey. Two of the proportional indices are inherently asymmetric about zero, and they inflate positive or negative effects, respectively. When predators exhibit a saturating functional response, the three proportional measures of per capita effect are biased toward a skewed distribution of interaction strengths dominated by effects on the rarest prey. Predator interference causes the per capita measures to emphasize the effects of rare predators. Estimates of per capita effects are also problematic when (1) the per capita effects are back-calculated from experiments designed to measure collective effects (e.g., predator exclusions), and (2) the collective effect of a predator is constant across a wide range of predator densities, as may be common for keystone predators. Finally, since all of the indices show time-dependent behavior, they are differentially suited for different experimental protocols (e.g., short-term vs. long-term results, or community initially near vs. far from equilibrium). All the indices explored here have the potential to provide useful, complementary information about ecological impacts of species in natural communities. In this analysis, we attempt to clarify what each index actually measures and the conditions under which each is most revealing.

Recent Internet measurements have found pervasive evidence of some surprising scaling properties. The two we focus on in this paper are self-similar scaling in the burst patterns of Internet traffic and, in some contexts, scale-free structure in the network's interconnection topology. These findings have led to a number of proposed models or “explanations” of such “emergent” phenomena. Many of these explanations invoke concepts such as fractals, chaos, or self-organized criticality, mainly because these concepts are closely associated with scale invariance and power laws. We examine these criticality-based explanations of self-similar scaling behavior—of both traffic flows through the Internet and the Internet's topology—to see whether they indeed explain the observed phenomena. To do so, we bring to bear a simple validation framework that aims at testing whether a proposed model is merely evocative, in that it can reproduce the phenomenon of interest but does not necessarily capture and incorporate the true underlying cause, or indeed explanatory, in that it also captures the causal mechanisms (why and how, in addition to what). We argue that the framework can provide a basis for developing a useful, consistent, and verifiable theory of large networks such as the Internet. Applying the framework, we find that, whereas the proposed criticality-based models are able to produce the observed “emergent” phenomena, they unfortunately fail as sound explanations of why such scaling behavior arises in the Internet.