Results for “Complex Network analysis” — Page 14

DOE OSTI2020

Heat Integration Optimization and Dynamic Modeling Investigation for Advancing the Coal-Direct Chemical Looping Process

The purpose of the project is to address the optimization and startup operation of a modular coal direct chemical looping (CDCL) combustion system integrated with a steam cycle for power generation to reduce the risks involved in further scale-up of the technology. The modular reactor design of the CDCL process provides flexibility in the fabrication of the reactor and in its operating capacity (i.e. turndown ratio) at the cost of a more complex heat exchange network (HEN) design and integration. To address the technology gaps and advance the efficiency and economic feasibility of the CDCL technology, the project will perform a detailed and comprehensive analysis of the integration of a modular CDCL reactor system and a steam cycle system under both static and transient conditions via HEN process performance simulations and system dynamic modeling, respectively. The scope of work consists of 1) Experimental and computational studies of the CDCL combustor reactor 2) Comprehensive static (i.e. steady-state) system HEN design analysis in CDCL 550 MWe commercial unit for power generation and 3) Dynamic modeling of site specific design of 10MWe CDCL large pilot plant. The project team has successfully developed and validated a kinetic model for the oxidation of oxygen carriers in the combustor using the unreacted shrinking core model (UCSM). The model is capable of capturing the oxidation kinetics of fully or partially reduced oxygen carrier particles. A computational fluid dynamics (CFD) model is developed to simulate the hydrodynamics, heat transfer, and chemical reaction occurring in the CDCL combustor. The model is developed in MFIX and ANSYS Fluent. Key aspects of CDCL combustor operation, including heat transfer, oxygen carrier oxidation, and the transport of oxygen carrier particles, are simulated using this CFD model. The HEN for a commercial scale 550 MWe CDCL power plant is simulated and optimized using ASPEN Plus. Practical design considerations are incorporated based on industrial experiences. The performance and cost for the commercial CDCL plant is updated based on these analyses. A dynamic model for the 10 MWe CDCL pilot plant is developed in ProTRAX simulation software. The model is based on the pilot plant design developed in project DE-FE0027654 “10 MWe CDCL Large Pilot Plang – Pre-FEED Study” and the steam cycle data obtained from Dover Light & Power plant. The transient behaviors during pilot plant load variation are simulated using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1993

MAC to VAX Connectivity: Heartrate Spectral Analysis System

The heart rate Spectral Analysis System (SAS) acquires and analyzes, in real-time, the Space Shuttle onboard electrocardiograph (EKG) experiment signals, calculates the heartrate, and applies a Fast Fourier Transformation (FFT) to the heart rate. The system also calculates other statistical parameters such as the 'mean heart rate' over specific time period and heart rate histogram. This SAS is used by NASA Principal Investigators as a research tool to determine the effects of weightlessness on the human cardiovascular system. This is also used to determine if Lower Body Negative Pressure (LBNP) is an effective countermeasure to the orthostatic intolerance experienced by astronauts upon return to normal gravity. In microgravity, astronauts perform the LBNP experiment in the mid deck of the Space Shuttle. The experiment data are downlinked by the orbiter telemetry system, then processed and analyzed in real-time by the integrated Life Sciences Data Acquisition (LSDS) - Spectral Analysis System. The data system is integrated within the framework of two different computer systems, VAX and Macintosh (Mac), using the networking infrastructure to assist the investigators in further understanding the most complex machine on Earth--the human body.

Rahman, Hasan H.↗

NASA NTRS1990

Neural net diagnostics for VLSI test

This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

Lin, T.↗

DOE OSTI2021

Nickel(ii) complexes based on dithiolate–polyamine binary ligand systems: crystal structures, hirshfeld surface analysis, theoretical study, and catalytic activity study on photocatalytic hydrogen generation

To ascertain the influence of binary ligand systems [1,1-dicyanoethylene-2,2-dithiolate (i-mnt -2 ) and polyamine {tetraen = tris(2-aminoethyl)amine, tren = diethylene triamine and opda = o-phenylenediamine}] on the coordination modes of the Ni(II) metal center and resulting supramolecular architectures, a series of nickel(II) thiolate complexes [Ni(tetraen)(i-mnt)](DMSO) (1), [Ni 2 (tren) 2 (i-mnt) 2 ] (2), and [Ni 2 (i-mnt) 2 (opda) 2 ] n (3) have been synthesized in high yield in one step in water and structurally characterized by single crystal X-ray crystallography and spectroscopic techniques. X-ray diffraction studies disclose the diverse i-mnt -2 coordination to the Ni +2 center in the presence of active polyamine ligands, forming a slightly distorted octahedral geometry (NiN 4 S 2 ) in 1, square planar (NiS 4 ) and distorted octahedral geometries (NiN 6 ) in the bimetallic co-crystallized aggregate of cationic [Ni(tren) 2 ] +2 and anionic [Ni(i-mnt) 2 ] -2 in 2, and a one dimensional (1D) polymeric chain along the [100] axis in 3, having consecutive square planar (NiS 4 ) and octahedral (NiN 6 ) coordination kernels. The N–H···O, N–H···S, N–H···N, N–H···S, N–H···N, and N–H···O type hydrogen bonds stabilize the supramolecular assemblies in 1, 2, and 3 respectively imparting interesting graph-set-motifs. The molecular Hirshfeld surface analyses (HS) and 2D fingerprint plots were utilized for decoding all types of non-covalent contacts in the crystal networks. Atomic HS analysis of the Ni +2 centers reveals significant Ni–N metal–ligand interactions compared to Ni–S interactions. We have also studied the unorthodox interactions observed in the solid state structures of 1–3 by QTAIM and NBO analyses. Moreover, all the complexes proved to be highly active water reduction co-catalysts (WRC) in a photo-catalytic hydrogen evolution process involving iridium photosensitizers, wherein 2 and 3 having a square planar arrangement around the nickel center(s) – were found to be the most active ones, achieving 1000 and 1119 turnover numbers (TON), respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Methods for Quantitative Thermal Analysis of Lithium Solid-State and Beyond Battery Safety

The use of differential scanning calorimetry (DSC) to measure the thermal behavior of individual components and electrolyte/electrode combinations is common. However, here we focus on DSC tests on an anode, cathode, and electrolyte (ACE) component combination over a temperature range that includes many of the phase transitions and key reactions (i.e., to 500 °C) that contribute to thermal runaway. This method can help quantify the complex reaction network in a full cell, thereby informing potential safety issues. Here, we used DSC heat flow data from a solid-state Li 0.43 CoO 2 +C+PVDF | LLZO | Li metal ACE sample and its components to quantify key factors affecting results. We focused on three areas: (1) ACE sample preparation and assembly in DSC pans, (2) DSC measurement parameters, and (3) heat flow analysis. Key points include the choice of component ratios (e.g., commercially relevant N:P capacity ratio), the importance of conductive carbon and binder, type of pan used, DSC ramp rate, and integration method used when dealing with broad and overlapping exothermic peaks. This work deepens the scientific basis and best practices for obtaining heat flow data from ACE samples for early-stage evaluation of solid-state and beyond battery safety.

25 ENERGY STORAGE↗

DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗

DOE OSTI2020

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

We report the accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Most existing theoretical works for generalization fail to explain the performance of neural networks in practice. To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness. We introduce the cover complexity (CC) to measure the difficulty of learning a data set and the inverse of the modulus of continuity to quantify neural network smoothness. A quantitative bound for expected accuracy/error is derived by considering both the CC and neural network smoothness. Although most of the analysis is general and not specific to neural networks, we validate our theoretical assumptions and results numerically for neural networks by several data sets of images. The numerical results confirm that the expected error of trained networks scaled with the square root of the number of classes has a linear relationship with respect to the CC. We also observe a clear consistency between test loss and neural network smoothness during the training process. In addition, we demonstrate empirically that the neural network smoothness decreases when the network size increases whereas the smoothness is insensitive to training dataset size.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

RivGraph: Automatic extraction and analysis of river and delta channel network topology

River networks sustain life and landscapes by carrying and distributing water, sediment, and nutrients throughout ecosystems and communities. At the largest scale, river networks drain continents through tree-like tributary networks. At typically smaller scales, river deltas and braided rivers form loopy, complex distributary river networks via avulsions and bifurcations.In order to model flows through these networks or analyze network structure, the topology, or connectivity, of the network must be resolved. Additionally, morphologic properties of each river channel as well as the direction of flow through the channel inform how fluxes travel through the network’s channels. Riv Graphis a Python package that automates the extraction and characterization of river channel networks from a user-provided binary image, or mask, of a channel network (Fig. 1). Masks may be derived from (typically remotely-sensed) imagery, simulations, or even hand-drawn. RivGraph will create explicit representations of the channel network by resolving river centerlines as links, and junctions as nodes. Flow directions are solved for each link of the network without using auxiliary data, e.g., a digital elevation model (DEM). Morphologic properties are computed as well, including link lengths, widths, sinuosities, branching angles,and braiding indices. If provided,RivGraph will preserve georeferencing information of the mask and will export results as ESRI shapefiles, GeoJSONs, and GeoTIFFs for easy import into GIS software.RivGraph can also return extracted networks as networkx objects for convenient interfacing with the full-featured networkx package (Hagberg et al., 2008). Finally, RivGraph offers a suite of topologic metrics that were specifically designed for river channel network analysis (Tejedor et al., 2015b).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗

NASA NTRS1989

Efficient utilization of graphics technology for space animation

Efficient utilization of computer graphics technology has become a major investment in the work of aerospace engineers and mission designers. These new tools are having a significant impact in the development and analysis of complex tasks and procedures which must be prepared prior to actual space flight. Design and implementation of useful methods in applying these tools has evolved into a complex interaction of hardware, software, network, video and various user interfaces. Because few people can understand every aspect of this broad mix of technology, many specialists are required to build, train, maintain and adapt these tools to changing user needs. Researchers have set out to create systems where an engineering designer can easily work to achieve goals with a minimum of technological distraction. This was accomplished with high-performance flight simulation visual systems and supercomputer computational horsepower. Control throughout the creative process is judiciously applied while maintaining generality and ease of use to accommodate a wide variety of engineering needs.

Panos, Gregory Peter↗

NASA NTRS2022

The hierarchical organization of autocatalytic reaction networks and its relevance to origin of life

Abiogenesis has long been suspected to require that chemical reaction networks contain multiple autocatalytic cores, but little is known about what features of these networks allow for the gradual accretion of complexity. To identify realistic scenarios for the emergence of life-like properties, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical “seeds”. We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.

Zhen Peng↗

DOE OSTI2024

Advancing set-conditional set generation: Diffusion models for fast simulation of reconstructed particles

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

4D structural changes and pore network model of biomass during pyrolysis

Biochar is an engineered carbon-rich substance used for soil improvement, environmental management, and other diverse applications. To date, the understanding of how biomass affects biochar microstructure has been limited due to the complexity of analysis involved in tracing the changes in the physical structure of biomass as it undergoes thermochemical conversion. In this study, we used synchrotron x-ray micro-tomography to visualize changes in the internal structure of biochar from diverse feedstock (miscanthus straw pellets, wheat straw pellets, oilseed rape straw pellets, and rice husk) during pyrolysis by collecting a sequence of 3D scans at 50 °C intervals during progressive heating from 50 °C to 800 °C. The results show a strong dependence of biochar porosity on feedstock as well as pyrolysis temperature, with observed porosity in the range of 7.41–60.56%. Our results show that the porosity, total surface area, pore volume, and equivalent diameter of the largest pore increases with increasing pyrolysis temperature up to about 550 °C. The most dramatic development of pore structure occurred in the temperature range of 350–450 °C. This understanding is pivotal for optimizing biochar’s properties for specific applications in soil improvement, environmental management, and beyond. By elucidating the nuanced variations in biochar’s physical characteristics across different production temperatures and feedstocks, this research advances the practical application of biochar, offering significant benefits in agricultural, environmental, and engineering contexts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Walking the ‘design–build–test–learn’ cycle: flux analysis and genetic engineering reveal the pliability of plant central metabolism

Oilseeds are of great economic importance for food and animal feed and their contribution to renewable energy production. Soybean seeds (Glycine max (L.) Merr.) contain c. 40% protein, 20% oil, and 30% carbohydrate (Song et al., 2023). Due to the massive scale of soybean production worldwide, even small improvements in seed protein and oil content make economic sense (Song et al., 2023). Successful manipulation of seed composition largely depends on a thorough understanding of the processes and pathways involved in the biosynthesis of fatty acids and amino acids, which are the building blocks of lipids and proteins. Rational engineering of the synthesis of storage reserves, that is, the rerouting of metabolic flux in central metabolism, is difficult to accomplish due to the complexity of the central metabolic network, the intricate regulation of its enzymes at multiple levels, and the often-unpredictable effects of genetic manipulation (Sweetlove et al., 2017). Therefore, the advancement of our understanding of central metabolism and its control of carbon partitioning requires following an iterative ‘design–build–test–learn’ (DBTL) cycle (Lin & Eudes, 2020) where metabolic flux analysis and hypothesis testing by transgenic approaches are important components. Previous metabolic studies on soybeans using isotopic tracers and metabolic flux analysis have provided insight into how lipid and protein biosynthesis occurs simultaneously during seed development (Allen et al., 2009; Allen & Young, 2013; Kambhampati et al., 2021). In an article published in this issue of New Phytologist, Morley et al. (2023; 1834–1851) put the insights they have gained into the delivery of metabolic precursors and energy cofactors to oil synthesis to the test and arrive at a successful metabolic engineering design. They show that an increase in seed oil content in soybeans can be achieved by overexpression of malic enzyme (ME) during seed development. Malic enzyme refers to a class of decarboxylating malate dehydrogenase enzymes that oxidize malate with NAD + or NADP + as redox cofactor while generating pyruvate and CO 2 . Like higher plants in general, soybean has distinct NADH- or NADPH-producing ME isoforms localized to the cytosol, plastid, or mitochondria (Gerrard Wheeler et al., 2016). As Morley et al. show, an increase in seed oil can be achieved in particular when a NADP+-dependent enzyme isoform (EC 1.1.1.40) is overexpressed in the plastid. Given the complex compartmentalization of pyruvate, malate, and redox metabolism (Fig. 1), increased oil production appears to depend on additional pyruvate and reducing equivalents being produced in the same compartment where de novo fatty acid biosynthesis occurs: the plastid.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Direct-to-Earth Communications with Mars Science Laboratory During Entry, Descent, and Landing

Mars Science Laboratory (MSL) undergoes extreme heating and acceleration during Entry, Descent, and Landing (EDL) on Mars. Unknown dynamics lead to large Doppler shifts, making communication challenging. During EDL, a special form of Multiple Frequency Shift Keying (MFSK) communication is used for Direct-To-Earth (DTE) communication. The X-band signal is received by the Deep Space Network (DSN) at the Canberra Deep Space Communication complex, then down-converted, digitized, and recorded by open-loop Radio Science Receivers (RSR), and decoded in real-time by the EDL Data Analysis (EDA) System. The EDA uses lock states with configurable Fast Fourier Transforms to acquire and track the signal. RSR configuration and channel allocation is shown. Testing prior to EDL is discussed including software simulations, test bed runs with MSL flight hardware, and the in-flight end-to-end test. EDA configuration parameters and signal dynamics during pre-entry, entry, and parachute deployment are analyzed. RSR and EDA performance during MSL EDL is evaluated, including performance using a single 70-meter DSN antenna and an array of two 34-meter DSN antennas as a back up to the 70-meter antenna.

Direct-to-Earth (DTE)↗