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Livestock production is the largest anthropogenic methane (CH₄) source globally over the decades. Enteric fermentation of ruminants is responsible for the majority of global livestock CH₄emissions. Both inventory-based models (IvtMs) and process-based models (PcMs) are extensively used to assess the livestock CH₄emission dynamics. However, the model performance and the associated uncertainty have not been well quantified and understood, which greatly hamper our credibility of the regional and global CH₄emission predictions. In this study, we compared the CH₄emissions of livestock enteric fermentation (CH_4,ef) predicted by multiple IvtMs and PcMs across Inner Mongolia, a region dominated by typical temperate grasslands that are widely used for animal husbandry. Twenty predictions from five IvtMs, and ten predations from five PcMs were explicitly calculated and compared for the reference year of 2006. The CH_4,efpredicted from PcMs is lower than IvtMs and the variation between PcMs is substantially higher, i.e. 0.34 ± 0.36 g CH₄/m²yr and 0.78 ± 0.14 g CH₄/m²yr for PcMs and IvtMs, respectively. Different model strategies undertaken, i.e. the demand-oriented strategy for IvtMs and the resource-demand co-determined one for PcMs, cause the different predictions of CH_4,efbetween the two model groups. Using the results from IvtMs as the baseline scalar, we identified and benchmarked the performance of individual PcMs in the study region. The quantitative information provided can facilitate the understanding of key principles and processes of CH_4,efestimations, which will contribute to the future model development of global CH₄emission.

 

We study the performance of a decentralized inte- gral control scheme for joint power grid frequency regulation and economic dispatch. We show that by properly designing the controller gains, after a power flow perturbation, the control achieves near-optimal economic dispatch while recovering the nominal frequency, without requiring any communication. We quantify the gap between the controllable power generation cost under the decentralized control scheme and the optimal cost, based on the DC power flow model. Moreover, we study the tradeoff between the cost and the convergence time, by adjusting parameters of the control scheme. Communication between generators reduces the convergence time. We identify key communication links whose failures have more significant impacts on the performance of a distributed power grid control scheme that requires information exchange between neighbors. 

Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data. 

Emerging research supports that triclosan (TCS), an antimicrobial agent found in thousands of consumer products, exacerbates colitis and colitis-associated colorectal tumorigenesis in animal models. While the intestinal toxicities of TCS require the presence of gut microbiota, the molecular mechanisms involved have not been defined. Here we show that intestinal commensal microbes mediate metabolic activation of TCS in the colon and drive its gut toxicology. Using a range of in vitro, ex vivo, and in vivo approaches, we identify specific microbial β-glucuronidase (GUS) enzymes involved and pinpoint molecular motifs required to metabolically activate TCS in the gut. Finally, we show that targeted inhibition of bacterial GUS enzymes abolishes the colitis-promoting effects of TCS, supporting an essential role of specific microbial proteins in TCS toxicity. Together, our results define a mechanism by which intestinal microbes contribute to the metabolic activation and gut toxicity of TCS, and highlight the importance of considering the contributions of the gut microbiota in evaluating the toxic potential of environmental chemicals.

 

We propose an interdependent random geometric graph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99% confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks. 

Complex systems such as smart cities and smart power grids rely heavily on their interdependent components. The failure of a component in one network may lead to the failure of the supported component in another network. Components which support a large number of interdependent components may be more vulnerable to attacks and failures. In this paper, we study the robustness of two interdependent networks under node failures. By modeling each network using a random geometric graph (RGG), we study conditions for the percolation of two interdependent RGGs after in-homogeneous node failures. We derive analytical bounds on the interdependent degree thresholds (k 1 ,k 2 ), such that the interdependent RGGs percolate after removing nodes in G i that support more than k j nodes in G j (∀i, j ∈ {1, 2}, i ≠ j). We verify the bounds using numerical simulation, and show that there is a tradeoff between k 1 and k 2 for maintaining percolation after the failures.