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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Volatiles from the necrophagous fly Cochliomyia macellaria (Diptera: Calliphoridae) as indicators of Salmonella exposure

Blow flies (Diptera: Calliphoridae) are crucial in forensic investigations due to their association with both living and dead humans and other animals. Additionally, their interactions with various resources and potential as vectors of pathogens of humans and other animals, thus, make them potential tools for biosurveillance. This study investigated the potential of monitoring volatile organic compounds (VOCs) emitted by blow flies exposed to Salmonella as a method for pathogen surveillance. Adult blow flies ( Cochliomyia macellaria ) were exposed, or not, to Salmonella enterica . Following exposure, VOCs released by the blow flies were collected and analyzed using gas chromatography-mass spectrometry (GC-MS). Results indicate a treatment by time interaction (P < 0.01). Indicator species analysis identified a single compound significantly associated with S. enterica exposure (P = 0.02), Nonane, 2,2,4,4,6,8,8-heptamethyl, potentially indicating an immune system response. Given a compound indicating exposure was detected, future research should determine if more replicates could detect more differences after Salmonella ingestion. This research highlights the potential of blow flies as biosurveillance tools and the potential value of volatiles for assessing their exposure to pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Earth's water reservoirs in a changing climate

Progress towards achieving a quantitative understanding of the exchanges of water between Earth's main water reservoirs is reviewed with emphasis on advances accrued from the latest advances in Earth Observation from space. These exchanges of water between the reservoirs are a result of processes that are at the core of important physical Earth-system feedbacks, which fundamentally control the response of Earth's climate to the greenhouse gas forcing it is now experiencing, and are therefore vital to understanding the future evolution of Earth's climate. The changing nature of global mean sea level (GMSL) is the context for discussion of these exchanges. Different sources of satellite observations that are used to quantify ice mass loss and water storage over continents, how water can be tracked to its source using water isotope information and how the waters in different reservoirs influence the fluxes of water between reservoirs are described. The profound influence of Earth's hydrological cycle, including human influences on it, on the rate of GMSL rise is emphasized. The many intricate ways water cycle processes influence water exchanges between reservoirs and thus sea-level rise, including disproportionate influences by the tiniest water reservoirs, are emphasized.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.

Mohammed, Ahmed↗
DOE OSTI2023

Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by “anomalies” or “non-idealities” such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide– and silicate–water interfaces. This critical review discusses how science progresses from understanding ideal solid–water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. Further, we anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Skin-integrated stretchable actuators toward skin-compatible haptic feedback and closed-loop human-machine interactions

Abstract Skin-integrated haptic interfaces that can relay a wealth of information from the machine to the human are of great interest. However, existing haptic devices are not yet able to produce haptic cues that are compatible with the skin. In this work, we present the stretchable soft actuators for haptic feedback, which can match the perception range, spatial resolution, and stretchability of the skin. Pressure-amplification structures are fabricated using a scalable self-assembly process to ensure an output pressure beyond the skin perception threshold. Due to the minimized device size, the actuator array can be fabricated with a sufficiently high spatial resolution, which makes the haptic device applicable for skin locations with the highest spatial acuity. A haptic feedback system is demonstrated by employing the developed soft actuators and highly sensitive pressure sensors. Two proof-of-concept applications are developed to illustrate the capability of transferring information related to surface textures and object shapes acquired at the robot side to the user side.

Chen, Si↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2022

Long-range current-induced spin accumulation in chiral crystals

Abstract Chiral materials, similarly to human hands, have distinguishable right-handed and left-handed enantiomers which may behave differently in response to external stimuli. Here, we use for the first time an approach based on the density functional theory (DFT)+PAOFLOW calculations to quantitatively estimate the so-called collinear Rashba–Edelstein effect (REE) that generates spin accumulation parallel to charge current and can manifest as chirality-dependent charge-to-spin conversion in chiral crystals. Importantly, we reveal that the spin accumulation induced in the bulk by an electric current is intrinsically protected by the quasi-persistent spin helix arising from the crystal symmetries present in chiral systems with the Weyl spin–orbit coupling. In contrast to conventional REE, spin transport can be preserved over large distances, in agreement with the recent observations for some chiral materials. This allows, for example, the generation of spin currents from spin accumulation, opening novel routes for the design of solid-state spintronics devices.

Roy, Arunesh↗
DOE OSTI2021

The algae revolution 2.0: the potential of algae for the production of food, feed, fuel and bioproducts – why we need it now

Algae made our world possible, and it can help us make the future more sustainable; but we need to change the way we live and adopt new more efficient production systems, and we need to do that now. When the world was new, the atmosphere was mainly carbon dioxide, and no animal life was possible. Along came algae with the process of photosynthesis, and things began to change. Ancient cyanobacteria algae turned carbon dioxide into enormous sums of lipids, proteins and carbohydrates, while they secreted oxygen into the atmosphere. Over a billion years, as oxygen filled the air and algae filled the seas, animal life became possible. Eventually all that algae biomass became petroleum and natural gas, which for eons sat undisturbed in vast underground reservoirs, holding enormous sums of untapped energy. Less than 200 years ago humans learned to tap these energy reserves to create the world we know today, but in so doing, we have released millions of years of stored CO 2 back into the atmosphere. Algae can again help make the world a better place, but this will require new thinking and new ways of producing our food, feed and fuels. We need an algae revolution 2.0.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Automatic detection of ship-induced cloud features in satellite imagery

Ships crossing the ocean are known to produce long, curvilinear features called ship tracks visible in satellite imagery via the Twomey effect; however, there has been little exploitation of satellite imagery for broad atmospheric studies or global monitoring of ship emissions due to the difficulty of automated ship track detection. Prior studies are either proof-of-concept, qualitatively assessed, or restricted to a certain time of day. We propose a statistical method for the automated identification of ship tracks and demonstrate using GOES-West ABI data. We first present a human-assisted segmentation method, which we use to generate a ground truth data set of 529 annotated ship tracks in GOES-West ABI products. We then describe a two-stage automated approach comprising a detection stage to generate ship track proposals and a classification stage to reduce false positives. For detection, we present a novel pipeline based around a z-score filtering technique, and for classification, we demonstrate several classifiers from literature. In a final experiment, we quantitatively tune the detection parameters and train the classifier using the ground truth dataset, then test on a sequestered set of images; the detect-then-classify system had an overall Pd of 0.68 and 0.80 for daytime and nighttime data, respectively, and the classifier reduced false positive detections by 67% and 75%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility–mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Further, other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Sequence similarity between SARS-CoV-2 nucleocapsid and multiple sclerosis-associated proteins provides insight into viral neuropathogenesis following infection

Abstract The novel coronavirus SARS-CoV-2 continues to cause death and disease throughout the world, underscoring the necessity of understanding the virus and host immune response. From the start of the pandemic, a prominent pattern of central nervous system (CNS) pathologies, including demyelination, has emerged, suggesting an underlying mechanism of viral mimicry to CNS proteins. We hypothesized that immunodominant epitopes of SARS-CoV-2 share homology with proteins associated with multiple sclerosis (MS). Using PEPMatch, a newly developed bioinformatics package which predicts peptide similarity within specific amino acid mismatching parameters consistent with published MHC binding capacity, we discovered that nucleocapsid protein shares significant overlap with 22 MS-associated proteins, including myelin proteolipid protein (PLP). Further computational evaluation demonstrated that this overlap may have critical implications for T cell responses in MS patients and is likely unique to SARS-CoV-2 among the major human coronaviruses. Our findings substantiate the hypothesis of viral molecular mimicry in the pathogenesis of MS and warrant further experimental exploration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

An in vitro BRAF activation assay elucidates molecular mechanisms driving disassembly of the autoinhibited BRAF state

The RAF kinases (ARAF, BRAF, and CRAF) are essential components of the RAS-ERK signaling pathway, which controls vital cellular processes and is frequently dysregulated in human disease. Notably, mutations that alter BRAF function are prominent drivers of human cancer and certain RASopathy disorders, making BRAF an important target for therapeutic intervention. Despite extensive research, several aspects of BRAF regulation remain unclear. In this study, we developed an in vitro BRAF activation assay using purified autoinhibited BRAF:14-3-3 2 :MEK complexes. Our results show that fully processed, active-state KRAS alone can promote dimer-dependent BRAF activation. Moreover, we found that phosphatidylserine (PS)-containing liposomes synergized with KRAS to promote BRAF activation, achieving activity levels comparable to those observed with BRAF proteins that constitutively dimerize. In contrast, the SMP phosphatase complex had only a minimal effect on BRAF catalytic activity in this system but mediated the dephosphorylation of the negative regulatory pS365 14-3-3 binding site in a manner that was accelerated by the presence of KRAS alone or KRAS and 30% PS liposomes. Finally, we show that inhibitors blocking the BRAF RBD:KRAS interaction were able to suppress the in vitro activation of BRAF, underscoring the critical role of RAS binding in initiating the disassembly of the BRAF autoinhibited state. Thus, this assay provides valuable insights into the steps required for BRAF activation and can serve as an effective screening tool for identifying compounds that may inhibit this process and have therapeutic potential.

BRAF↗
DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2025

Hestia-SWIFL: hourly anthropogenic fossil fuel CO2 and heat on the 2km WRF grid, version 1.1

The Hestia-SWIFL version 1.1 anthropogenic heat (AH) and fossil fuel CO2 (FFCO2) emissions data product represent emissions due to the combustion of fossil fuel and cement production within the state of Arizona from 2019 to 2022. This product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address extreme heat, air quality, climate change and related urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions are generated using a bottom-up/engineering approach and are tied to results generated by the Vulcan Project version 4, an effort to quantify space/time-resolved FFCO2 & AH emissions for the entire United States landscape. A large number of data sources are combined to best estimate the emissions at fine scales such as air quality emissions data, traffic flow data, building information, sociodemographic information, and fuel statistics. The AH product provides emissions for two emissions sources (transportation and point source emissions) in units of Watts per hour per square meter (W/m2) per year (annual files) or per hour (hourly files). The FFCO2 product provides emissions from nine individual emission sectors as well as the total, and in units of tons of carbon (tC) per grid cell per year or per hour. The output made available here places the native spatial resolution of the Hestia FFCO2 & AH emissions data product (points, lines, and polygons) into a regularized 2km x 2km grid at hourly and annual temporal resolutions, and stored in netCDF files. The exact spatial extent is defined by the ASU Weather Research Forecast (WRF) simulation grid. All data are processed using R/Python pm high-performance computing system. 2-27-2026 updates: Bugs in airport hourly profile (both AH and FFCO2) and building spatial patterns (FFCO2 only) were fixed. Hourly emissions are reprocessed for all years to reflect those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗