Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “recurrence”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Steady Spills, Stronger Signals: Machine Learning for Slow Spill Analysis

Particle accelerator experiments rely on stable, consistent proton beams to maximize scientific discovery. This presentation introduces beam spills, duty factor, and beam stability using a meteor shower analogy before exploring how feedback control and machine learning, including recurrent neural networks (RNNs), can analyze spill data, identify patterns, and predict beam behavior. Together, these approaches support beam optimization and improve our understanding of accelerator performance for experiments such as Mu2e.

Prescott, Matthew J. [Fermilab]↗
DOE OSTI2023

Data and code repository for "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?"

There has been an increasing need for forecasting power generation at the sub-seasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In the study "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?", we identify four WRs that influence wind resources over North America using a self-organizing map (SOM) algorithm. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 50% of the monthly variance of power production over the western United States, and the explanatory power generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales. This repository includes all the data and codes we use for analyses in this study. Users may use them to reproduce the results of this study on their end.

17 WIND ENERGY↗
DOE OSTI2021

Information Entropy as Quantifier of Potential Predictability in the Tropical Indo-Pacific Basin

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Neutralizing epitopes on Clostridioides difficile toxin A revealed by the structures of two camelid VHH antibodies

Toxin A (TcdA) and toxin B (TcdB) are two key virulence factors secreted by Clostridioides difficile, which is listed as an urgent threat by the CDC. These two large homologous exotoxins are mainly responsible for diseases associated with C. difficile infection (CDI) with symptoms ranging from diarrhea to life threatening pseudomembranous colitis. Single-domain camelid antibodies (VHHs) AH3 and AA6 are two potent antitoxins against TcdA, which when combined with two TcdB-targeting VHHs showed effective protection against both primary and recurrent CDI in animal models. Here, we report the co-crystal structures of AH3 and AA6 when they form complexes with the glucosyltransferase domain (GTD) and a fragment of the delivery and receptor-binding domain (DRBD) of TcdA, respectively. Based on these structures, we find that AH3 binding enhances the overall stability of the GTD and interferes with its unfolding at acidic pH, and AA6 may inhibit the pH-dependent conformational changes in the DRBD that is necessary for pore formation of TcdA. These studies reveal two functionally critical epitopes on TcdA and shed new insights into neutralizing mechanisms and potential development of epitope-focused vaccines against TcdA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a Fusarium ear rot resistance gene in maize by QTL mapping and RNA sequencing

Fusarium ear rot (FER) caused by Fusarium verticillioides is a prevalent maize disease. To comprehensively characterize the genetic basis of the natural variation in FER resistance, a recombinant inbred line (RIL) population was used to map quantitative trait loci (QTL) for FER resistance. A total of 17 QTL were identified by linkage mapping in eight environments. These QTL were located on six chromosomes and explained 3.88–15.62% of the total phenotypic variation. Moreover, qFER1.03 had the strongest effect and accounted for 4.98–15.62% of the phenotypic variation according to analyses of multiple environments involving best linear unbiased predictions. The chromosome segment substitution lines (CSSLs) derived from a cross between Qi319 (donor parent) and Ye478 (recurrent parent) were used to verify the contribution of qFER1.03 to FER resistance. The line CL171, which harbored an introgressed qFER1.03 , was significantly resistant to FER. Further fine mapping of qFER1.03 revealed that the resistance QTL was linked to insertion/deletion markers InDel 8 and InDel 2, with physical distances of 43.55 Mb and 43.76 Mb, respectively. Additionally, qFER1.03 differed from the previous resistance QTL on chromosome 1. There were three annotated genes in this region. On the basis of the RNA-seq data, which revealed the genes differentially expressed between the FER-resistant Qi319 and susceptible Ye478, GRMZM2G017792 (MPK3) was preliminarily identified as a candidate gene in the qFER1.03 region. The Pr-CMV-VIGS system was used to decrease the GRMZM2G017792 expression level in CL171 by 34–57%, which led to a significant decrease in FER resistance. Using RIL and CSSL populations combined with RNA-seq and Pr-CMV-VIGS, the candidate gene can be dissected effectively, which provided important gene resource for breeding FER-resistant varieties.

Xia, Yusheng↗
DOE OSTI2022

Incorporating polar field data for improved solar flare prediction

In this paper, we consider incorporating data associated with the sun’s north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle’s intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Characterization of Oral Candida spp. Biofilms in Children and Adults Carriers from Eastern Europe and South America

Background: Candida albicans and non-Candida albicans Candida species (NCACs) are known to colonize and invade various tissues, including the oral mucosa. In this work, we aimed to characterize mature biofilms of several Candida spp. clinical isolates (n = 33) obtained from the oral mucosa of children, adults, and elders of Eastern Europe and South America. Methods: Each strain was evaluated for its capacity to form biofilms in terms of total biomass using the crystal violet assay and for matrix components production (proteins and carbohydrates) using the BCA and phenol-sulfuric tests, respectively. The effect of different antifungals on biofilm formation was studied. Results: in the children’s group, a predominance of C. krusei (81%) was observed, while, among adults, the main species was C. albicans (59%). Most strains showed a reduced response to antimicrobial drugs when in biofilm form (p < 0.01). Moreover, it was observed that strains isolated from children produced more matrix, with higher levels of protein and polysaccharides. Conclusions: children were more likely to be infected by NCACs than adults. More importantly, these NCACs were able to form biofilms richer in matrix components. This finding is of clinical importance, particularly in pediatric care, since stronger biofilms are highly associated with antimicrobial resistance, recurrent infections, and higher therapeutic failure.

Alves, Anelise Maria Costa Vasconcelos (ORCID:0000↗
DOE OSTI2020

Detection of Extreme Phenomena in the Stable Boundary Layer over the Amazonian Forest

We apply different methods for detection of extreme phenomena (EP) in air-turbulent time series measured in the nocturnal boundary layer above the Amazon forest. The methods used were: (a) a Morlet complex wavelet transform, which is often used in analysis of non-linear application processes. Through the use of the wavelet, it is possible to observe a phase singularity that involves a strong interaction between an extensive range of scales; (b) recurrence plot tests, which were used to identify a sudden change between different stable atmospheric states. (c) statistical analysis of early-warning signals, which verify simultaneous increases in the autocorrelation function and in the variance in the state variable; and (d) analysis of wind speed versus turbulent kinetic energy to identify different turbulent regimes in the stable boundary layer. We found it is adequate to use a threshold to classify the cases of strong turbulence regime, as a result of the occurrence of EP in the tropical atmosphere. All methods used corroborate and indicate synergy between events that culminate in what we classify as EP of the stable boundary layer above the tropical forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling Down Syndrome Myeloid Leukemia by Sequential Introduction of GATA1 and STAG2 Mutations in Induced Pluripotent Stem Cells with Trisomy 21

Children with Down syndrome (DS) have a high risk for acute myeloid leukemia (DS-ML). Genomic characterization of DS-ML blasts showed the presence of unique mutations in GATA1, an essential hematopoietic transcription factor, leading to the production of a truncated from of GATA1 (GATA1s). GATA1s, together with trisomy 21, is sufficient to develop a pre-leukemic condition called transient abnormal myelopoiesis (TAM). Approximately 30% of these cases progress into DS-ML by acquisition of additional somatic mutations in a stepwise manner. We previously developed a model for TAM by introducing disease-specific GATA1 mutation in trisomy 21-induced pluripotent stem cells (iPSCs), leading to the production of N-terminally truncated short form of GATA1 (GATA1s). In this model, we used CRISPR/Cas9 to introduce a co-operating mutation in STAG2, a member of the cohesin complex recurrently mutated in DS-ML but not in TAM. Hematopoietic differentiation of GATA1 STAG2 double-mutant iPSC lines confirmed GATA1s expression and the loss of functional STAG2 protein, leading to enhanced production of immature megakaryocytic population compared to GATA1 mutant alone. Megakaryocyte-specific lineage expansion of the double-mutant HSPCs exhibited close resemblance to the DS-ML immunophenotype. Transcriptome analysis showed that GATA1 mutation resulted in downregulation of megakaryocytic and erythrocytic differentiation pathways and interferon α/β signaling, along with an upregulation of pathways promoting myeloid differentiation such as toll-like receptor cascade. The co-occurrence of STAG2 knockout partially reverted the expression of genes involved in myeloid differentiation, likely leading to enhanced self-renewal and promoting leukemogenesis. In conclusion, we developed a DS-ML model via hematopoietic differentiation of gene-targeted iPSCs bearing trisomy 21.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗
DOE OSTI2024

Influence of Karen Immigrant Students on Teachers’ Instructional Decisions in the Rural United States

Across the United States, the number of classrooms welcoming young students who are new English language learners (ELL) is rapidly growing. Motivated by a dramatic increase in the number of Myanmar- and Burma-originating immigrants now attending English-speaking schools in the rural, upper midwestern United States, this study systematically considers how the presence of Karen immigrant students impacts the instructional decisions that K-12 classroom teachers make using an exploratory case study method. Several recurrent themes emerged, and findings can be used to help better prepare both pre-service and in-service teachers to meet the needs of their ELL students, as well as policy makers. These emergent themes include the following: (a) authentic teacher–family relationships are key; (b) visual/hands-on learning and the use of technology enhance student achievement; (c) student work must be completed during the school day in classrooms; (d) teachers’ purposeful use of parallel language is critical for learning; (e) rigor can be maintained, although quantity is often adjusted; and (f) targeted ESL/cultural training for pre-service and newly hired teachers is positively influential. Such informed training often seems to help teachers learn key cultural distinctions and become better able to identify the most effective ways to meet the needs of students.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Earthquake Nowcasting with Deep Learning

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS.

Fox, Geoffrey Charles (ORCID:0000000310171391)↗
DOE OSTI2021

Disastrous Flash Floods Triggered by Moderate to Minor Rainfall Events. Recent Cases in Coastal Benguela (Angola)

The present work focuses on two recent flash floods in coastal Benguela (Angola), both triggered by moderate rainfall but which had disastrous consequences for local populations (namely 71 deaths in 2015 and 17 in 2019). The research involved a regional survey to establish the effects of these floods combined with a geomorphological and socio-economic analysis of the most affected areas to understand the main forcing factors. The two flash floods produced major damage in restricted sectors within very small coastal catchments (<16 km2). The prevalence of fine-grained sedimentary rocks, relatively steep hills, thin soil cover, and vegetation scarcity are natural factors that promote surface runoff. However, socio-economic conditions are most likely the main reasons of flood damage. Namely, rapid population growth with poor planning and making use of low-quality construction materials, the high waste yields that are not properly managed and the absence of flood risk awareness. In the small valleys around the fast-growing cities of coastal Benguela, hazardous flash floods occur recurrently, even after moderate precipitation. Most affected areas are determined by local conditions that compromise drainage at the time of the rainfall event, being very difficult to predict.

Dinis, Pedro A. (ORCID:0000000175587369)↗
DOE OSTI2022

Analyzing BMP2, FGFR, and TGF Beta Expressions in High-Grade Osteosarcoma Untreated and Treated Autografts Using Proteomic Analysis

In the last few decades, biological reconstruction techniques have improved greatly for treating high-grade osteosarcoma patients. To conserve the limb, and its function the affected tumor-bearing bones have been treated using liquid nitrogen and irradiation processes that enable the removal of entire tumors from the bone, and these treated autografts can be reconstructed for the patients. Here, we focus on the expressions of the growth factor family proteins from the untreated and treated autografts that play a crucial role in bone union, remodeling, and regeneration. In this proteomic study, we identify several important cytoskeletal, transcriptional, and growth factor family proteins that showed substantially low levels in untreated autografts. Interestingly, these protein expressions were elevated after treating the tumor-bearing bones using liquid nitrogen and irradiation. Therefore, from our preliminary findings, we chose to determine the expressions of BMP2, TGF-Beta, and FGFR proteins by the target proteomics approach. Using a newly recruited validation set, we successfully validate the expressions of the selected proteins. Furthermore, the increased growth factor protein expression after treatment with liquid nitrogen may contribute to bone regeneration healing, assist in faster recovery, and reduce local recurrence and metastatic spread in high-grade sarcoma patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Bilton, Kyle J. (ORCID:0000000184553689)↗
DOE OSTI2022

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L ∞ norm and less than 6.3% in the L 1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.

60 APPLIED LIFE SCIENCES↗