GiRaFFE: An Open-Source General Relativistic Force-Free Electrodynamics Code

We present GiRaFFE, the first open-source general relativistic force-free electrodynamics (GRFFE) code for dynamical, numerical-relativity generated spacetimes. GiRaFFE adopts the strategy pioneered by McKinney and modified by Paschalidis and Shapiro to convert a GR magnetohydrodynamic (GRMHD) code into a GRFFE code. In short, GiRaFFE exists as a modification of IllinoisGRMHD, a user-friendly, open-source, dynamical-spacetime GRMHD code. Both GiRaFFE and IllinoisGRMHD leverage the Einstein Toolkit's highly-scalable infrastructure to make possible large-scale simulations of magnetized plasmas in strong, dynamical spacetimes on adaptive-mesh refinement (AMR) grids. We demonstrate that GiRaFFE passes a large suite of both flat and curved-spacetime code tests passed by a number of other state-of-the-art GRFFE codes, and is thus ready for production-scale simulations of GRFFE phenomena of key interest to relativistic astrophysics.Comment: 23 pages, 4 figures. Consistent with published versio

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

Segmenting root systems in X-ray computed tomography images using level sets

The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.Comment: 11 page

Metal-insulator transition and orbital reconstruction in Mott quantum wells of NdNiO3_{3}

The metal-insulator transition (MIT) and the underlying electronic and orbital structure in $e_{g}^{1}$ quantum wells based on NdNiO$_{3}$ was investigated by d.c. transport and resonant soft x-ray absorption spectroscopy. By comparing quantum wells of the same dimension but with two different confinement structures, we explicitly demonstrate that the quantum well boundary condition of correlated electrons is critical to selecting the many-body ground state. In particular, the long-range orderings and the MIT are found to be strongly enhanced under quantum confinement by sandwiching NdNiO$_{3}$ with the wide-gap dielectric LaAlO$_{3}$, while they are suppressed when one of the interfaces is replaced by a surface (interface with vacuum). Resonant spectroscopy reveals that the reduced charge fluctuations in the sandwich structure are supported by the enhanced propensity to charge ordering due to the suppressed $e_g$ orbital splitting when interfaced with the confining LaAlO$_{3}$ layer

PLM and design education: a collaborative experiment on a mechanical device

The authors would like to thanks Ms Andia Montes C. and Mr Nelson J. for their helpful suggestions received during this experimentThe shift from sequential to concurrent engineering has initiated changes in the way design projects are managed. In order to assist designers, numerous effective tools have been devised for collaborative engineering, which are also well suited to the business world. Faced with these new challenges, practices in design training must evolve to allow students to be mindful of these evolutions as well as to be able to manage projects in these new work environments. After presenting a state of the art of collaborative tools used in product design, our paper presents an experiment focusing on the codesign of a complex mechanical product. This experiment was carried out between two centers of the Arts et Metiers ParisTech School of Engineering, located in Paris and Angers. We analyze the results obtained in this experiment and discuss some ways to improve future projects for inter-centre training programs in design engineering.The shift from sequential to concurrent engineering has initiated changes in the way design projects are managed. In order to assist designers, numerous effective tools have been devised for collaborative engineering, which are also well suited to the business world. Faced with these new challenges, practices in design training must evolve to allow students to be mindful of these evolutions as well as to be able to manage projects in these new work environments. After presenting a state of the art of collaborative tools used in product design, our paper presents an experiment focusing on the codesign of a complex mechanical product. This experiment was carried out between two centers of the Arts et Metiers ParisTech School of Engineering, located in Paris and Angers. We analyze the results obtained in this experiment and discuss some ways to improve future projects for inter-centre training programs in design engineering

Should We Expect Each Year in the Next Decade (2019–28) to be Ranked Among the Top 10 Warmest Years Globally?

Annual rankings of global temperature are widely cited by media and the general public, not only to place the most recent year in a historical perspective, but also as a first-order metric of recent climate change that is easily digestible by the general public. Moreover, all annual NOAAGlobalTemp anomalies from 1880 (the earliest reading available) through the mid-1970s are well below anomalies of the top 10 warmest years in Table 1, even when considering the uncertainty of the NOAAGlobalTemp time series values. While we expect the algorithm\u27s performance to be largely independent of any changes made to the way that NOAAGlobalTemp (or any other annual global temperature time series) is calculated, we do envision monitoring the algorithm\u27s performance and proposing future fine tuning of the algorithm if warranted. Similarly, the AR with trend extension approach (and the AR without trend extension approach to a lesser extent) appears to slightly outperform the AR+ENSO approach in terms of simulation error and prediction interval width, but again the differences are not statistically significant

Robust Environmental Mapping by Mobile Sensor Networks

Constructing a spatial map of environmental parameters is a crucial step to preventing hazardous chemical leakages, forest fires, or while estimating a spatially distributed physical quantities such as terrain elevation. Although prior methods can do such mapping tasks efficiently via dispatching a group of autonomous agents, they are unable to ensure satisfactory convergence to the underlying ground truth distribution in a decentralized manner when any of the agents fail. Since the types of agents utilized to perform such mapping are typically inexpensive and prone to failure, this results in poor overall mapping performance in real-world applications, which can in certain cases endanger human safety. This paper presents a Bayesian approach for robust spatial mapping of environmental parameters by deploying a group of mobile robots capable of ad-hoc communication equipped with short-range sensors in the presence of hardware failures. Our approach first utilizes a variant of the Voronoi diagram to partition the region to be mapped into disjoint regions that are each associated with at least one robot. These robots are then deployed in a decentralized manner to maximize the likelihood that at least one robot detects every target in their associated region despite a non-zero probability of failure. A suite of simulation results is presented to demonstrate the effectiveness and robustness of the proposed method when compared to existing techniques.Comment: accepted to icra 201