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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Prenatal and postnatal polybrominated diphenyl ether exposure and visual spatial abilities in children

Polybrominated diphenyl ethers (PBDEs) are associated with impaired visual spatial abilities in toxicological studies, but no epidemiologic study has investigated PBDEs and visual spatial abilities in children. The Health Outcomes and Measures of the Environment Study, a prospective birth cohort (2003–2006, Cincinnati, OH), was used to examine prenatal and childhood PBDEs and visual spatial abilities in 199 children. PBDEs were measured at 16±3 weeks gestation and at 1, 2, 3, 5, and 8 years using gas chromatography/isotope dilution high-resolution mass spectrometry. We used the Virtual Morris Water Maze to measure visual spatial abilities at 8 years. In covariate-adjusted models, 10-fold increases in BDE-47, −99, and −100 at 5 years were associated with shorter completion times by 5.2 s (95% Confidence Interval [CI] −9.3, −1.1), 4.5 s (95% CI −8.1, −0.9), and 4.7 s (95% CI −9.0, −0.3), respectively. However, children with higher BDE-153 at 3 years had longer completion times (β=5.4 s, 95% CI −0.3, 11.1). Prenatal PBDEs were associated with improved visual spatial memory retention, with children spending a higher percentage of their search path in the correct quadrant. Child sex modified some associations between PBDEs and visual spatial learning. Longer path lengths were observed among males with increased BDE-47 at 2 and 3 years, while females had shorter paths. In conclusion, prenatal and postnatal BDE-28, −47, −99, and −100 at 5 and 8 years were associated with improved visual spatial abilities, whereas a pattern of impairments in visual spatial learning was noted with early childhood BDE-153 concentrations. - Highlights: • The VMWM test was used to assess visual spatial abilities in children at 8 years. • BDE-153 at 3 years was adversely associated with visual spatial learning. • BDE-47, −99, and −100 at 5 years was associated with better visual spatial learning. • Prenatal PBDEs were associated with improved visual spatial memory retention. • Male children were observed to perform more poorly on the VMWM than females.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Discovery of phase-locked variable polarization in an RS Canum Venaticorum-like star, HR 5110

Using measurements on 79 nights in 1980 January-August, it is established that the polarization in the ultraviolet (U band) varies synchronously on the known binary period of 2.6132 days, with a peak-to-peak amplitude of 0.03%. The pattern is mainly second harmonic in character, as typical of binary star examples such as Algol and Upsilon Her. Reflection by the cooler star, by a plasma concentration, or both, is suggested. The scale of the polarization seems to be consistent with Rayleigh scattering by neutral hydrogen on the cooler star, and a roughly lambda to the -4th wavelength dependence should be searched for. The QU curve suggests a sizeable inclination for the system, of about 45 deg, but this is preliminary. Separately, a possible weak periodicity might be present on a subsidiary period, conceivably related to a rotating star spot.

Barbour, M. S.↗
NASA NTRS1996

Turbulent Fluid Motion 6: Turbulence, Nonlinear Dynamics, and Deterministic Chaos

Several turbulent and nonturbulent solutions of the Navier-Stokes equations are obtained. The unaveraged equations are used numerically in conjunction with tools and concepts from nonlinear dynamics, including time series, phase portraits, Poincare sections, Liapunov exponents, power spectra, and strange attractors. Initially neighboring solutions for a low-Reynolds-number fully developed turbulence are compared. The turbulence is sustained by a nonrandom time-independent external force. The solutions, on the average, separate exponentially with time, having a positive Liapunov exponent. Thus, the turbulence is characterized as chaotic. In a search for solutions which contrast with the turbulent ones, the Reynolds number (or strength of the forcing) is reduced. Several qualitatively different flows are noted. These are, respectively, fully chaotic, complex periodic, weakly chaotic, simple periodic, and fixed-point. Of these, we classify only the fully chaotic flows as turbulent. Those flows have both a positive Liapunov exponent and Poincare sections without pattern. By contrast, the weakly chaotic flows, although having positive Liapunov exponents, have some pattern in their Poincare sections. The fixed-point and periodic flows are nonturbulent, since turbulence, as generally understood, is both time-dependent and aperiodic.

Deissler, Robert G.↗
DOE OSTI2015

Phonon Sensor Dynamics for Cryogenic Dark Matter Search Experiment

Understanding the quasiparticle diffusion process inside sputtered aluminum (Al thin films (∼ 0.1-1 μm is critical for the Cryogenic Dark Matter Search (CDMS experiment to further optimize its detectors to directly search for dark matter. An initial study with Al films was undertaken by our group ∼ 20 years ago, but some important questions were not answered at the time. This thesis can be considered a continuation of that critical study. The CDMS experiment utilizes high purity silicon and germanium crystals to simultaneously measure ionization and phonons created by particle interactions. In addition to describing some of the rich physics involved in simultaneously detecting ionization and phonons with a CDMS detector, this thesis focuses on the detailed physics of the phonon sensors themselves, which are patterned onto CDMS detector surfaces. CDMS detectors use thin sputtered Al films to collect phonon energy when it propagates to the surfaces of the detector crystals. The phonon energy breaks Cooper pairs and creates quasiparticles (qps). These qps diffuse until they get trapped in an proximitized “overlap” region where lower-Tc tungsten films connect to the Al film. These tungsten films are the transition edge sensors (W-TESs CDMS uses to readout phonon signals. We performed a wide range of experiments using several sets of test devices designed and fabricated specifically for this work. The devices were used mostly to study quasiparticle (qp transport in Al films and qp transmission through Al/W interfaces. The results of this work are being used to optimize the design of detectors for SuperCDMS SNOLAB. This thesis is intended for CDMS collaborators who are interested in knowing more about the detailed fundamentals of how our phonon sensors work so they can take full advantage of their benefits. However, this work can also be read by general readers who are interested in particle detection using TES technology. This thesis contains eight chapters. The first chapter gives basic background information about dark matter and searches for it. We then describe the basic CDMS detector technology in Chapter two. Chapter three focuses on superconductivity and explains some of the solid state physic most relevant to our Al and W film studies. We then turn our attention to the fabrication processes used to make test devices, and describe some of the studies done to characterize our W and Al film properties. Chapter five explains the experimental setup including how a 3He/4He dilution refrigerator works, and how our electronics were configured. We then get to chapter six where we present key experimental results. Chapter seven covers the TES model we used for our test devices to simulate the data pulse shapes and reconstruct the pulse energies. We also describe the diffusion models used to fit our data. Finally, we end with a short summary of our findings and provide a few suggestions for future studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases

Magnetotactic bacteria (MTB) are prokaryotes that possess genes for the synthesis of membrane-bounded crystals of magnetite or greigite, called magnetosomes. Despite over half a century of studying MTB, only about 60 genomes have been sequenced. Most belong to Proteobacteria, with a minority affiliated with the Nitrospirae, Omnitrophica, Planctomycetes, and Latescibacteria. Due to the scanty information available regarding MTB phylogenetic diversity, little is known about their ecology, evolution and about the magnetosome biomineralization process. This study presents a large-scale search of magnetosome biomineralization genes and reveals 38 new MTB genomes. Several of these genomes were detected in the phyla Elusimicrobia, Candidatus Hydrogenedentes, and Nitrospinae, where magnetotactic representatives have not previously been reported. Analysis of the obtained putative magnetosome biomineralization genes revealed a monophyletic origin capable of putative greigite magnetosome synthesis. The ecological distributions of the reconstructed MTB genomes were also analyzed and several patterns were identified. These data suggest that open databases are an excellent source for obtaining new information of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents

Purpose: Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure the highest benefit of screening mammography for patients. The authors have previously proposed a computer-aided education system based on trainee models. Those models relate human-assessed image characteristics to trainee error. In this study, the authors propose to build trainee models that utilize features automatically extracted from images using computer vision algorithms to predict likelihood of missing each mass by the trainee. This computer vision-based approach to trainee modeling will allow for automatically searching large databases of mammograms in order to identify challenging cases for each trainee. Methods: The authors’ algorithm for predicting the likelihood of missing a mass consists of three steps. First, a mammogram is segmented into air, pectoral muscle, fatty tissue, dense tissue, and mass using automated segmentation algorithms. Second, 43 features are extracted using computer vision algorithms for each abnormality identified by experts. Third, error-making models (classifiers) are applied to predict the likelihood of trainees missing the abnormality based on the extracted features. The models are developed individually for each trainee using his/her previous reading data. The authors evaluated the predictive performance of the proposed algorithm using data from a reader study in which 10 subjects (7 residents and 3 novices) and 3 experts read 100 mammographic cases. Receiver operating characteristic (ROC) methodology was applied for the evaluation. Results: The average area under the ROC curve (AUC) of the error-making models for the task of predicting which masses will be detected and which will be missed was 0.607 (95% CI,0.564-0.650). This value was statistically significantly different from 0.5 (p < 0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p = 0.0009). Therefore, generally the authors’ models were able to predict which masses were detected and which were missed better than chance. Conclusions: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Status of ProtoDUNE Dual Phase

The Deep Underground Neutrino Experiment (DUNE) is a dual-site experiment for long baseline neutrino oscillation studies, and for neutrino astrophysics and nucleon decay searches. DUNE will comprise four 10 kton fiducial liquid argon time-projection-chamber (LAr TPC) modules placed at the Sanford Underground Research Facility (South Dakota, USA). One of these modules will profit form the dual phase (DP) technology where the charge is extracted, amplified, and detected in gaseous argon above the liquid surface allowing a fine readout pitch, a low energy threshold, and good pattern reconstruction of the events. To gain experience in building and operating such a large-scale DP LAr detector, a prototype is currently being assembled at the CERN Neutrino Platform. The ProtoDUNE-DP detector consists of a 6x6x6 m3 LAr TPC and commissioning started in Summer 2019.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1975

Emotionality in response to aircraft noise: A report of development work

A literature search and pilot study conducted to investigate the topic of emotional response to aircraft noise are described. A Tell-A-Story Technique was developed for use in the pilot study which required respondents to make up stories for a series of aircraft-related and non-aircraft-related pictures. A content analysis of these stories was made. The major finding was that response patterns varied among three groups of respondents - those currently living near airports, those who had lived near airports in the past, and those who had never lived near airports. Negative emotional feelings toward aircraft were greatest among respondents who had lived near airports in the past but no longer did. A possible explanation offered for this finding was that people currently living near airports might adapt to the situation by denying some of their negative feelings, which they might feel more free to express after they had moved away from the situation. Other techniques used in the pilot study are also described, including group interviews and a word association task.

Klaus, P. A.↗
DOE OSTI2021

Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study

Background Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. Objective We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. Methods We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query. Results PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. Conclusions The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.</:p>

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1985

A search for diffuse interstellar bands in the ultraviolet

The International Ultraviolet Explorer (IUE) Satellite was used to collect interstellar extinction curves for 58 moderately reddened lines of sight. Spectral types of the background stars ranged from O7 to A0. All luminosity classes are represented, but early B main-sequence stars predominate. E(B-V) reddening values ranged from 0.14 to 1.13 mag, with an average of 0.63 mag. These extinction curves, less some 10 curves judged of lower quality, were co-added in order to reduce the random and fixed-pattern noise of the instrument. The resulting averaged curve was examined for evidence of small-scale structure, with negative results. The 2-sigma limits were found to be 0.09 mag for wavelengths between 1150 A and 2000 A, and 0.16 mag for wavelengths between 2000 A and 2300 A. Because of mismatch errors in weak stellar lines, it is unlikely that much improvement in these results is possible. The implications of these negative results are discussed with particular reference to the MgO theory of interstellar grains and diffuse bands.

Seab, C. G.↗
DOE OSTI2013

'L=R' -- $U(1)_R$ Lepton Number at the LHC

We perform a detailed study of a variety of LHC signals in supersymmetric models where lepton number is promoted to an (approximate) U(1)( )R( ) symmetry. Such a symmetry has interesting implications for naturalness, as well as flavor- and CP-violation, among others. Interestingly, it makes large sneutrino vacuum expectation values phenomenologically viable, so that a slepton doublet can play the role of the down-type Higgs. As a result, (some of) the leptons and neutrinos are incorporated into the chargino and neutralino sectors. This leads to characteristic decay patterns that can be experimentally tested at the LHC. The corresponding collider phenomenology is largely determined by the new approximately conserved quantum number, which is itself closely tied to the presence of “leptonic R-parity violation”. We find rather loose bounds on the first and second generation squarks, arising from a combination of suppressed production rates together with relatively small signal efficiencies of the current searches. Naturalness would indicate that such a framework should be discovered in the near future, perhaps through spectacular signals exhibiting the lepto-quark nature of the third generation squarks. The presence of fully visible decays, in addition to decay chains involving large missing energy (in the form of neutrinos) could give handles to access the details of the spectrum of new particles, if excesses over SM background were to be observed. The scale of neutrino masses is intimately tied to the source of U(1)( )R( ) breaking, thus opening a window into the R-breaking sector through neutrino physics. Further theoretical aspects of the model have been presented in the companion paper [1].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2024

Structural phase diagram for Sm-substituted BiFeO 3 multiferroics

The structural evolution of Sm substituted BiFe⁢O 3 is studied by total x-ray scattering and structure modeling. It is shown that the crystal structure changes from polar to antipolar and then to nonpolar when the Sm to Bi ratio in the material approaches 20% and 40%, respectively, with no intermixing between the structure types. The evolution is driven by lattice strain induced by the difference in the size of Sm and Bi atoms, leading to changes in the pattern of octahedral tilts and Bi off-centering, which, in turn, induce changes in the multiferroic properties. Furthermore, the substitution ratio at which the different structure types emerge appears to be tied up with the average radius of the atomic species occupying the Bi sites in the perovskite lattice and volume occupied by a formula unit, rendering both quantities useful predictor variables for guiding computational searches for substituted BiFe⁢O 3 multiferroics with improved functional properties.

Ferroelectricity↗
NASA NTRS1993

Theoretical calculations on the electron absorption spectra of selected Polycyclic Aromatic Hydrocarbons (PAH) and derivatives

As a theoretical component of the joint effort with the laboratory of Dr. Lou Allamandola to search for potential candidates for interstellar organic carbon compound that are responsible for the visible diffuse interstellar absorption bands (DIB's), quantum mechanical calculations were performed on the electron absorption spectra of selected polycyclic aromatic hydrocarbons (PAH) and derivatives. In the completed project, 15 different species of naphthalene, its hydrogen abstraction and addition derivatives, and corresponding cations and anions were studied. Using semiempirical quantum mechanical method INDO/S, the ground electronic state of each species was evaluated with restricted Hartree-Fock scheme and limited configuration interaction. The lowest energy spin state for each species was used for electron absorption calculations. Results indicate that these calculations are accurate enough to reproduce the spectra of naphthalene cation and anion observed in neon matrix. The spectral pattern of the hydrogen abstraction and addition derivatives predicted based on these results indicate that the electron configuration of the pi orbitals of these species is the dominant determinant. A combined list of 19 absorptions calculated from 4500 A to 10,400 A were compiled and suggested as potential candidates that are relevant for the DIB's absorptions. Continued studies on pyrene and derivatives revealed the ground state symmetries and multiplicities of its neutral, anionic, and cationic species. Spectral calculations show that the cation (B(sub 3g)-2) and the anion (A(sub u)-2) are more likely to have low energy absorptions in the regions between 10 kK and 20 kK, similar to naphthalene. These absorptions, together with those to be determined from the hydrogen abstraction and addition derivatives of pyrene, can be used to provide additional candidates and suggest experimental work in the search for interstellar compounds that are responsible for DIB's.

Du, Ping↗
DOE OSTI2012

A LARGE SYSTEMATIC SEARCH FOR CLOSE SUPERMASSIVE BINARY AND RAPIDLY RECOILING BLACK HOLES

We have carried out a systematic search for subparsec supermassive black hole (BH) binaries among z {approx}< 0.7 Sloan Digital Sky Survey quasars. These are predicted by models of supermassive BH and host galaxy coevolution, therefore their census and population properties constitute an important test of these models. In our working hypothesis, one of the two BHs accretes at a much higher rate than the other and carries with it the only broad emission line region of the system, making the system analogous to a single-lined spectroscopic binary star. Accordingly, we used spectroscopic principal component analysis to search for broad H{beta} emission lines that are displaced from the quasar rest frame by |{Delta} v| {approx}> 1000 km s{sup -1}. This method also yields candidates for rapidly recoiling BHs. Of the 88 candidates, several were previously reported in the literature. We found a correlation between the peak offset and skewness of the broad H{beta} profiles, suggesting a common physical explanation for these profiles. We carried out follow-up spectroscopic observations of 68 objects to search for changes in the peak velocities of the H{beta} lines. We measured statistically significant changes in 14 objects, with implied accelerations between -120 and +120 km s{sup -1} yr{sup -1}. Interpreting the offset broad emission lines as signatures of supermassive binaries is subject to many caveats. Many more follow-up observations over a long temporal baseline are needed to characterize the variability pattern of the broad lines and test that it is consistent with orbital motion. The possibility that some of the objects in this sample are rapidly recoiling BHs remains open.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

AR-CITE: Analysis of Search Results for the Clarification and Identification of Technology Emergence

The Analysis of Search Results for the Clarification and Identification of Technology Emergence (AR-CITE) computer code examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e. scholarly publications and citation, world patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the subject domain to be clarified and identified.

Source record↗
DOE OSTI2023

Accelerating scientific discoveries through data-driven innovations

Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.

97 MATHEMATICS AND COMPUTING↗