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At least 37 records · Page 2

One of These Things IS Like the Other: Pursuing a New Taxonomy of Industry for Improved Energy System Modeling

Industrial processes drive the exchange of materials, energy, and currency throughout the economy. These processes are powered by electricity and direct combustion, with variation in their operation even within the same industry. This heterogeneity makes it difficult for large models, including the National Energy Modeling System (US), to project their energy use while remaining tractable. Decarbonization and ensuing changes to the energy system require changes to industrial processes while offering opportunities for process innovation, but the extent and nature of changes are difficult to model with current classification schemes and corresponding data. The North American Industrial Classification (NAICS) is an economic taxonomy of industries, but its categories are less meaningful from an energy and material flow perspective. For example, a facility that makes steel from iron ore in a blast furnace/basic oxygen furnace is categorized under the same NAICS code as a facility that makes steel from scrap in an electric arc furnace despite the scale, use of recycled scrap versus iron ore, and energy use differences in the two facility types. Exploratory analysis is performed on a large dataset used for plant-level energy assessment in order to detect clusters that can aid in better modeling of industry for energy analysis in an evolving system with breakthrough technologies.

28 EE - Advanced Manufacturing Office (EE-5A)↗

Radio galaxy zoo EMU: towards a semantic radio galaxy morphology taxonomy

We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We address the limitations of common radio galaxy morphology classifications by applying this approach. We experimentally derive a set of semantic tags for the Radio Galaxy Zoo EMU (Evolutionary Map of the Universe) project and the wider astronomical community. We collect 8486 plain English annotations of radio galaxy morphology, from which we derive a taxonomy of tags. The tags are plain English. The result is an extensible framework, which is more flexible, more easily communicated, and more sensitive to rare feature combinations, which are indescribable using the current framework of radio astronomy classifications.

79 ASTRONOMY AND ASTROPHYSICS↗

Real-Time Coupling of Geographically Distributed Research Infrastructures: Taxonomy, Overview, and Real-World Smart Grid Applications

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

distributed laboratories↗

A Taxonomy for Classification and Comparison of Dataflows for GNN Accelerators

Recently, Graph Neural Networks (GNNs) have received a lot of interest because of their success in learning representations from graph structured data. However, GNNs exhibit different compute and memory characteristics compared to traditional Deep Neural Networks (DNNs). Graph convolutions require feature aggregations from neighboring nodes (known as the aggregation phase), which leads to highly irregular data accesses. GNNs also have a very regular compute phase that can be broken down to matrix multiplications (known as the combination phase). All recently proposed GNN accelerators utilize different dataflows and microarchitecture optimizations for these two phases. Different communication strategies between the two phases have been also used. However, as more custom GNN accelerators are proposed, the harder it is to qualitatively classify them and quantitatively contrast them. In this work, we present a taxonomy to describe several diverse dataflows for running GNN inference on accelerators. This provides a structured way to describe and compare the design-space of GNN accelerators.

97 MATHEMATICS AND COMPUTING↗

A Taxonomy and Feature set for Server-Side Identification of Proxies

Malicious actors frequently use proxies and VPNs to evade detection and hide their origin. Current challenges to information security include the use of residential proxies to blend in with normal traffic and Man-in-the-Middle phishing proxies that are used to compromise accounts protected with mult-factor authentication. We advance a taxonomy and feature set for the identification of proxied traffic based on the network layer where proxying occurs. We describe how these features apply to common proxy types and how to use these features in the classification of the proxied traffic. Collection of these additional features is feasible using existing network sensors and web servers, while only adding about 30% volume to commonly deployed network sensor logs.

97 MATHEMATICS AND COMPUTING↗

Whither the genus Caldicellulosiruptor and the order Thermoanaerobacterales: phylogeny, taxonomy, ecology, and phenotype

The order Thermoanaerobacterales currently consists of fermentative anaerobic bacteria, including the genus Caldicellulosiruptor. Caldicellulosiruptor are represented by thirteen species; all, but one, have closed genome sequences. Interest in these extreme thermophiles has been motivated not only by their high optimal growth temperatures (≥70°C), but also by their ability to hydrolyze polysaccharides including, for some species, both xylan and microcrystalline cellulose. Caldicellulosiruptor species have been isolated from geographically diverse thermal terrestrial environments located in New Zealand, China, Russia, Iceland and North America. Evidence of their presence in other terrestrial locations is apparent from metagenomic signatures, including volcanic ash in permafrost. Here, phylogeny and taxonomy of the genus Caldicellulosiruptor was re-examined in light of new genome sequences. Based on genome analysis of 15 strains, a new order, Caldicellulosiruptorales, is proposed containing the family Caldicellulosiruptoraceae, consisting of two genera, Caldicellulosiruptor and Anaerocellum. Furthermore, the order Thermoanaerobacterales also was re-assessed, using 91 genome-sequenced strains, and should now include the family Thermoanaerobacteraceae containing the genera Thermoanaerobacter, Thermoanaerobacterium, Caldanaerobacter, the family Caldanaerobiaceae containing the genus Caldanaerobius, and the family Calorimonaceae containing the genus Calorimonas. A main outcome of ANI/AAI analysis indicates the need to reclassify several previously designated species in the Thermoanaerobacterales and Caldicellulosiruptorales by condensing them into strains of single species. Comparative genomics of carbohydrate-active enzyme inventories suggested differentiating phenotypic features, even among strains of the same species, reflecting available nutrients and ecological roles in their native biotopes.

59 BASIC BIOLOGICAL SCIENCES↗

Taxonomy and Identification of Principal Foliar Nematode Species ( Aphelenchoides and Litylenchus )

Nematodes are Earth’s most numerous multicellular animals and include species that feed on bacteria, fungi, plants, insects, and animals. Foliar nematodes are mostly pathogens of ornamental crops in greenhouses, nurseries, forest trees, and field crops. Nematode identification has traditionally relied on morphological and anatomical characters using light microscopy and, in some cases, scanning electron microscopy (SEM). This review focuses on morphometrical and brief molecular details and key characteristics of some of the most widely distributed and economically important foliar nematodes that can aid in their identification. Aphelenchoides genus includes some of the most widely distributed nematodes that can cause crop damages and losses to agricultural, horticultural, and forestry crops. Morphological details of the most common species of Aphelenchoides (A. besseyi, A. bicaudatus, A. fragariae, A. ritzemabosi) are given with brief molecular details, including distribution, identification, conclusion, and future directions, as well as an updated list of the nominal species with its synonyms. Litylenchus is a relatively new genus described in 2011 and includes two species and one subspecies. Species included in the Litylenchus are important emerging foliar pathogens parasitizing trees and bushes, especially beech trees in the United States of America. Brief morphological details of all Litylenchus species are provided.

54 ENVIRONMENTAL SCIENCES↗

Internal Reaction Mass Taxonomy and Narrow-Down Study

Internal reaction mass (IRM) wave energy converters (WEC) capture wave energy by reacting against a moving mass that is suspended within their hulls. Due to their hermetically sealed Power Take Off (PTO), compact form factor and relatively simple mooring requirements, the National Renewable Energy Laboratory has identified IRM WECs are good candidates for applications such as ocean observation. A literature review of existing IRM mechanisms lead to the classification of IRM WECs based on their principal method of energy capture and principal axes of operation. The IRM categories include sliding mass, pendular and gyroscopic devices. A narrow down study was conducted to ascertain the most promising mechanisms for further investigation; devices were evaluated based on a decision matrix of weighted criteria that included device power output, prototypability and power to mass ratio. The narrow down study identified the issue of achieving resonance with pendulum-based designs, as well as the poor response produced by reaction to long period waves. Chaotic pendula and methods to multiply agitation were identified as a means to increase power output. Alternative methods of power generation, such as triboelectric nanogenerators, were also identified as a technology that would benefit from multiplied agitation and chaotic motion. This work was funded under the U.S. Department of Energy's Water Power Technologies Office's Powering the Blue Economy portfolio.

ENGINEERING,TIDAL AND WAVE POWER↗

A taxonomy of automatic differentiation pitfalls

Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples such as chaos, time-averages, discretizations, fixed-point loops, lookup tables, linear solvers, and probabilistic programs, in the hope that readers may more easily avoid or detect such pitfalls. We also review debugging techniques and their effectiveness in these situations.

Autodiff↗

Separate sensible and latent cooling: Carnot limits and system taxonomy

Energy efficiency and thermal comfort can be improved by independent control of temperature and humidity. In recent work, an emerging class of cooling systems known as separate sensible and latent cooling (SSLC) systems have demonstrated 14 to 47% energy savings, while simultaneously providing better comfort control than conventional air conditioning. In this work, SSLC is defined as using two or more cooling processes with unequal sensible heat ratio (SHR) to control temperature and humidity independently and dynamically. Fundamental efficiency limits of SSLC systems are developed for the first time, demonstrating that the Carnot efficiency for SSLC is higher than that of conventional systems. It is revealed that SSLC has particularly high potential for moderate outdoor temperatures, high sensible building loads, and drier indoor conditions. Additionally, this work proposes a classification scheme for SSLC systems, revealing an enormous number of feasible cycle permutations. This provides a framework for the important research task of developing SSLC systems that will save energy, be practical to construct, and be controllable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Phylogenomic insights into the taxonomy, ecology, and mating systems of the lorchel family Discinaceae (Pezizales, Ascomycota)

Lorchels, also known as false morels (Gyromitra sensu lato), are iconic due to their brain-shaped mushrooms and production of gyromitrin, a deadly mycotoxin. Molecular phylogenetic studies have hitherto failed to resolve deep-branching relationships in the lorchel family, Discinaceae, hampering our ability to settle longstanding taxonomic debates and to reconstruct the evolution of toxin production. We generated 75 draft genomes from cultures and ascomata (some collected as early as 1960), conducted phylogenomic analyses using 1542 single-copy orthologs to infer the early evolutionary history of lorchels, and identified genomic signatures of trophic mode and mating-type loci to better understand lorchel ecology and reproductive biology. Our phylogenomic tree was supported by high gene tree concordance, facilitating taxonomic revisions in Discinaceae. We recognized 10 genera across two tribes: tribe Discineae (Discina, Maublancomyces, Neogyromitra, Piscidiscina, and Pseudodiscina) and tribe Gyromitreae (Gyromitra, Hydnotrya, Paragyromitra, Pseudorhizina, and Pseudoverpa); Piscidiscina was newly erected and 26 new combinations were formalized. Paradiscina melaleuca and Marcelleina donadinii formed their own family-level clade sister to Morchellaceae, which merits further taxonomic study. Genome size and CAZyme content were consistent with a mycorrhizal lifestyle for the truffle species (Hydnotrya spp.), whereas the other Discinaceae genera possessed genomic properties of a saprotrophic habit. Lorchels were found to be predominantly heterothallic-either MAT1-1 or MAT1-2-but a single occurrence of colocalized mating-type idiomorphs indicative of homothallism was observed in Gyromitra esculenta strain CBS101906 and requires additional confirmation and follow-up study. Lastly, we confirmed that gyromitrin has a phylogenetically discontinuous distribution, having been detected exclusively in two distantly related genera (Gyromitra and Piscidiscina) belonging to separate tribes. Our genomic dataset will facilitate further investigations into the gyromitrin biosynthesis genes and their evolutionary history. With additional sampling of Geomoriaceae and Helvellaceae-two closely related families with no publicly available genomes-these data will enable comprehensive studies on the independent evolution of truffles and ecological diversification in an economically important group of pezizalean fungi.

Dirks, Alden C↗

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗