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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dispersion Leverage Coronagraph: a nulling coronagraph for use on primary objective grating telescopes

Here, we present the Dispersion Leverage Coronagraph (DLC), a variation of the Achromatic Interfero Coronagraph (AIC) that is designed for optical systems featuring large, dispersive primary objective gratings. DLC was originally designed for the Diffractive Interfero Coronagraph Exoplanet Resolver (DICER), a notional 20 m class infrared space telescope utilizing the enhanced one-dimensional angular resolution of large diffraction gratings to discover and characterize near-Earth exoplanets. Here we develop the theoretical foundation for DLC, and apply it to DICER as an example use case. We derive important properties of the DLC system, including focal plane transmission maps, stellar leakage, residual optical path difference tolerance, and pointing error/jitter considerations. Ultimately, we found that DLC effectively nulls an on-axis target across the entire spectrum in the focal plane, allowing for 2D/λ diffraction-limited imaging. It requires asymmetrical fine-guidance tolerances on pointing error/jitter. We work through a benchmark DICER design, explaining the need for a second disperser to reduce background from Zodiacal light, and showing that it could plausibly find and characterize ∼4 nearby, habitable exoplanets around Sun-like stars in a 7-year mission; about 30% of the habitable exoplanets within 8 pc were found in our simulation. The DLC may be useful for any application requiring extremely high-resolution, close companion spectroscopy.

AIC↗
DOE OSTI2025

Multifrequency Models of Black Hole Photon Rings from Low-luminosity Accretion Disks

Images of black holes encode both astrophysical and gravitational properties. Detecting highly lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced from synchrotron emission from 10 to 670 GHz. As an application, we systematically study several disk models and compute their total flux density, average radii, and optical depth. The model parameters are chosen around fiducial values calibrated to general relativistic magnetohydrodynamic (GRMHD) simulations and observations of M87*. For the parameter space studied, we characterize the transition between optically thin and thick regimes and the frequency at which the first photon ring is observable. Our results highlight the need for careful definitions of photon ring radius in the image domain, as in certain models the highly lensed photon ring is dimmer than the direct emission at certain angles. We find that at low frequencies the ring radii are set by the electron temperature, while at higher frequencies the magnetic field strength plays a more significant role, demonstrating how multifrequency analysis can also be used to infer plasma parameters. Lastly, we show how our implementation can qualitatively reproduce multifrequency black hole images from GRMHD simulations when adding time variability to our disk model through Gaussian random fields. This approach provides a new method for simulating observations from the Event Horizon Telescope and the proposed Black Hole Explorer space mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers

This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Antihelium-3 sensitivity for the GRAMS experiment

The Gamma-Ray and AntiMatter Survey (GRAMS) is a next-generation balloon/satellite mission utilizing a Liquid Argon Time Projection Chamber (LArTPC) detector to measure both MeV gamma rays and antinuclei produced by dark matter annihilation or decay. The GRAMS can identify antihelium-3 events based on the measurements of X-rays and charged pions from the decay of the exotic atoms, Time of Flight (TOF), energy deposition, and stopping range. This paper shows the antihelium-3 sensitivity estimation using a GEANT4 Monte Carlo simulation. For the proposed long-duration balloon (LDB) flight program (35 days x 3 flights) and future satellite mission (2-year observation/10-year observation), the sensitivities become 1.47 × 10 −7 [m 2 s sr GeV/n] −1 and 1.55 × 10 −9 [m 2 s sr GeV/n] −1 /3.10 x 10 -10 [m 2 s sr GeV/n] −1 , respectively. The results indicate that GRAMS can extensively investigate various dark matter models through the antihelium-3 measurements.

Antihelium-3↗
DOE OSTI2025

An analytical model of “Electron-Only” magnetic reconnection rates

Abstract “Electron-only” reconnection, which is both uncoupled from the surrounding ions and much faster than standard reconnection, is arguably ubiquitous in turbulence. One critical step to understanding the rate in this novel regime is to model the outflow speed that limits the transport of the magnetic flux, which is super ion Alfvénic but significantly lower than the electron Alfvén speed based on the asymptotic reconnecting field. Here we develop a simple model to determine this limiting speed by taking into account the multiscale nature of reconnection, the Hall-mediated electron outflow speed, and the pressure buildup within the small system. The predicted scalings of rates and various key quantities compare well with fully kinetic simulations and can be useful for interpreting the observations of NASA’s Magnetospheric-Multiscale (MMS) mission and other ongoing missions.

Physics↗
DOE OSTI2026

An Open Source Perovskite Solar Cell Curve Tracer

When developing systems for perovskite solar cells (PSCs), it is important to have a testing platform that closely resembles your deployed systems. While developing a 1 U CubeSat, the Big Red Sat-1 at the University of Nebraska-Lincoln, we found a lack of passive curve tracing instrumentation, which we needed for our mission. To that end, we developed a benchtop passive curve tracer that leverages the Ossila Solar Simulator as an AM1.5 light source. This device utilizes a ladder of switched resistors and a precision voltage and current measurement to achieve the curve tracing. It also integrates a common-anode multiplexer to switch through multipixel PSCs. Finally, the solar cell holder is separate from the main measurement system, so various substrates sizes and pixel counts can be supported. This system is tested using various PSCs. It has been open sourced to enable reuse in future systems that may benefit from the lower power consumption and flight heritage.

14 SOLAR ENERGY↗
DOE OSTI2025

Earthquake detection in a simulated lunar regolith using distributed acoustic sensing

Current models of inner lunar geology have largely been inferred from the seismic experiments and observations performed during the Apollo missions that comprised a relatively small number of seismic instruments. Refining constraints on fundamental lunar relationships such as crust-mantle and mantle-core boundaries in the future will require seismic arrays spanning larger epicentral distances. A promising technology for installing dense seismic arrays with minimal human effort is distributed acoustic sensing (DAS), an approach that allows a single length of fiber optic cable to act as hundreds or thousands of sensors when coupled with a DAS interrogator. While terrestrial uses of DAS technology for seismic monitoring rely on burying the cable to maximize fidelity of seismic signal transmission to the fiber, digging meters of trench to bury optical fiber on lunar or planetary surfaces is logistically infeasible. To evaluate DAS signal attenuation due to surface deployment of cable in lunar regolith, we completed earthquake detection analyses that evaluated the sensitivity of an optic-fiber DAS system to seismic signals at different burial depths. We deployed a single-mode fiber in a 10-m open-bottom wooden box filled with a lunar regolith simulant (LRS) with fiber buried at different depths within the LRS and recorded signals for four regional and local earthquakes. The results were used to identify and evaluate signal attenuation in surface-deployed fiber compared to buried fiber in the LRS. Burial depth responses to active-source signals were also evaluated similar to previous studies characterizing DAS sensitivity of surface-deployed fiber. Atmospheric noise was minimal as the cable was deployed in an indoor environment; however, where observed, atmospheric and anthropogenic noise was filtered out using the same bandpass filtering used to identify earthquake events. We found that signal attenuation of the surface-deployed fiber compared to buried fiber was relatively high in active-source experiments but was not consistently observed in earthquake signals. That burial depth is not highly correlated to attenuation of the observed earthquake signals indicates that in a noise-limited environment, placing DAS-interrogated fiber directly at the regolith surface may be a promising deployment strategy to consider for sensing remote seismic signals during lunar exploration.

58 GEOSCIENCES↗
DOE OSTI2024

Simulated Microgravity Alters Gene Regulation Linked to Immunity and Cardiovascular Disease

Microgravity exposure induces a cephalad fluid shift and an overall reduction in physical activity levels which can lead to cardiovascular deconditioning in the absence of countermeasures. Future spaceflight missions will expose crew to extended periods of microgravity among other stressors, the effects of which on cardiovascular health are not fully known. In this study, we determined cardiac responses to extended microgravity exposure using the rat hindlimb unloading (HU) model. We hypothesized that exposure to prolonged simulated microgravity and subsequent recovery would lead to increased oxidative damage and altered expression of genes involved in the oxidative response. To test this hypothesis, we examined hearts of male (three and nine months of age) and female (3 months of age) Long–Evans rats that underwent HU for various durations up to 90 days and reambulated up to 90 days post-HU. Results indicate sex-dependent changes in oxidative damage marker 8-hydroxydeoxyguanosine (8-OHdG) and antioxidant gene expression in left ventricular tissue. Three-month-old females displayed elevated 8-OHdG levels after 14 days of HU while age-matched males did not. In nine-month-old males, there were no differences in 8-OHdG levels between HU and normally loaded control males at any of the timepoints tested following HU. RNAseq analysis of left ventricular tissue from nine-month-old males after 14 days of HU revealed upregulation of pathways involved in pro-inflammatory signaling, immune cell activation and differential expression of genes associated with cardiovascular disease progression. Taken together, these findings provide a rationale for targeting antioxidant and immune pathways and that sex differences should be taken into account in the development of countermeasures to maintain cardiovascular health in space.

Genetics & Heredity↗
DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Effects of 5-ion 6-beam sequential irradiation in the presence and absence of hindlimb or control hindlimb unloading on behavioral performances and plasma metabolic pathways of Fischer 344 rats

Introduction Effects and interactions between different spaceflight stressors are expected to be experienced by crew on missions when exposed to microgravity and galactic cosmic rays (GCRs). One of the limitations of previous studies on simulated weightlessness using hindlimb unloading (HU) is that a control HU condition was not included. Methods We characterized the behavioral performance of male Fischer rats 2 months after sham or total body irradiation with a simplified 5-ion 6-mixed-beam exposure representative of GCRs in the absence or presence of HU. Six months later, the plasma, hippocampus, and cortex were processed to determine whether the behavioral effects were associated with long-term alterations in the metabolic pathways. Results In the open field without and with objects, interactions were observed for radiation × HU. In the plasma of animals that were not under the HU or control HU condition, the riboflavin metabolic pathway was affected most for sham irradiation vs. 0.75 Gy exposure. Analysis of the effects of control HU on plasma in the sham-irradiated animals showed that the alanine, aspartate, glutamate, riboflavin, and glutamine metabolisms as well as arginine biosynthesis were affected. The effects of control HU on the hippocampus in the sham-irradiated animals showed that the phenylalanine, tyrosine, and tryptophan pathway was affected the most. Analysis of effects of 0.75 Gy irradiation on the cortex of control HU animals showed that the glutamine and glutamate metabolic pathway was affected similar to the hippocampus, while the riboflavin pathway was affected in animals that were not under the control HU condition. The effects of control HU on the cortex in sham-irradiated animals showed that the riboflavin metabolic pathway was affected. Animals receiving 0.75 Gy of irradiation showed impaired glutamine and glutamate metabolic pathway, whereas animals receiving 1.5 Gy of irradiation showed impaired riboflavin metabolic pathways. A total of 21 plasma metabolites were correlated with the behavioral measures, indicating that plasma and brain biomarkers associated with behavioral performance are dependent on the environmental conditions experienced. Discussion Phenylalanine, tyrosine, and tryptophan metabolism as well as phenylalanine and tryptophan as plasma metabolites are biomarkers that can be considered for spaceflight as they were revealed in both Fischer and WAG/Rij rats exposed to simGCRsim and/or HU.

Physiology↗
DOE OSTI2024

Advanced Simulation and Computing: FY25 Implementation Plan

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Vitrification of Hanford Tank 241-AN-107 Waste and Equivalent Simulant

Hanford Site nuclear waste is to be vitrified at the Waste Treatment and Immobilization Plant (WTP), which is a part of the safe and efficient retrieval, treatment, and disposal mission of the U.S. Department of Energy Office of River Protection. Hanford tank 241-AN-107 (referred to herein as AN-107) is one of the initial Hanford radioactive tank wastes planned to be processed and vitrified. A portion of AN-107 waste was retrieved by Washington River Protection Solutions, LLC (WRPS) and transferred to Pacific Northwest National Laboratory (PNNL). Compared to previously received and vitrified wastes (AP-107, AP-105, and AP-105), the concentration of organics in AN-107 was greater by an order of magnitude, while the activity of radionuclides was multiple orders of magnitude greater.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Vitrification of Hanford Tank 241-AW-105 Waste and Equivalent Simulant

Hanford Site nuclear waste is to be vitrified at the Waste Treatment and Immobilization Plant (WTP), which is a part of the safe and efficient retrieval, treatment, and disposal mission of the U.S. Department of Energy - Hanford Field Office. A portion of Hanford tank 241-AW-105 (referred to herein as AW-105) waste was retrieved by Hanford Tank Waste Operations and Closure (H2C) and transferred to Pacific Northwest National Laboratory (PNNL). Compared to previously received and vitrified wastes (AP-107, AP-101, AN-107 and AP-105), the concentration of potassium in AW-105 was greater by an order of magnitude.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Position Papers for Inverse Methods for Complex Systems under Uncertainty Workshop

The ability to solve inverse problems – inferring unknown parameters, structures, or states of a system from observed data – is essential for advancing scientific discovery and innovation capabilities for the DOE mission. Basic research needs and challenges are particularly acute in emerging areas such as the interactive, data-driven, modeling and simulation of digital twins; decision support for experiments at DOE scientific user facilities; and for other complex systems and workflows. Inverse problems are at the heart of understanding and controlling complex systems due to factors such as observational data with varying modalities and fidelities, inherent uncertainties in physical measurements and numerical models, and the computational demands of rapid and high-fidelity simulations. The convergence of recent scientific computing trends – scientific machine learning, artificial intelligence, and computing advances such as exascale computing – is creating unprecedented opportunities. These advancements offer the potential to revolutionize how we approach inverse problems to extract actionable insights with the required level of accuracy and computational efficiency. This workshop and the Call for Position Papers are vital steps in bringing together experts to collectively explore and identify the new computational and mathematical directions needed in inverse methods for complex systems under uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗
DOE OSTI2026

Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model

Providing robust real time flood warnings is of paramount importance to coastal communities. Although state-of-the-art hydrodynamic models are capable of robustly predicting Coastal Water Levels (CWL), unresolved drivers affecting level fluctuations are often not represented by the model governing equations. This work evaluates a novel method to improve the performance of the ADvanced CIRCulation (ADCIRC) hydrodynamic model by assimilating observations from four nadir-only satellite altimetry missions against a set of National Oceanic and Atmospheric Administration (NOAA) gauge stations located across the entire U.S. East Coast. Two different types of simulations were performed – Open Loop (OL) and Data Assimilation (DA). Five different simulations were performed where four different satellite altimetry observations were assimilated individually and combined with two different scenarios – with and without considering the data quality flags. Results indicate that, despite their limited spatial coverage, merging nadir-only observations into ADCIRC from the newly launched Surface Water and Ocean Topography (SWOT)’s nadir altimeter can improve the model performance at 76% of the gauge locations, whereas Sentinel-6 improves 73% of the total locations, Jason-3 74%, and SARAL 21%. Furthermore, combining observations from SWOT-nadir, Jason-3, and Sentinel-6 can improve the ADCIRC performance at more than 80% of the gauge locations for 107-day simulation. Nadir-only satellite altimetry observations can be useful for improving the model performance even if flagged as “poor quality” near the coast. When the flagged data are disregarded, SWOT can improve ADCIRC at 78%, Sentinel-6 at 73%, Jason-3 at 53%, and SARAL at 21% of the gauge locations. The ability to improve the model simulations largely depends on the availability of a satellite overpass nearby. Therefore, model performance can be further enhanced if satellite observations are available during a storm surge event, stressing the importance of frequent satellite overpasses.

Aafnan Bhuiyan, Soelem↗
DOE OSTI2024

Data-driven closure modeling for hypersonic turbulent flows

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This report summarizes work performed from FY22-FY24 focused on improving RANS models for hypersonic flows using data-driven modeling and scientific machine learning. In this work we: 1. Investigate the current capabilities of RANS models in Sandia’s parallel aerodynamics and re-entry code (SPARC) for hypersonic flows with a focus on shock boundary layer interactions (SBLIs), 2. Assess several established corrections that exist in the literature aimed at improving predictions for SBLIs, 3. Develop improved models for the Reynolds stress tensor using tensor-basis neural networks, 4. Develop a neural-network-based variable turbulent Prandtl number model to reduce errors in wall heating in SBLIs. 5. Begin future investigations including employing the LIFE framework to improve wall heating predictions in SBLIs as well as the ensemble Kalman filter. We find that current RANS models in SPARC are deficient for complex SBLI flows. In particular, no current model jointly predicts wall heat flux, wall shear stress, and wall pressure with reasonable accuracy. Existing corrections help, but do not alleviate this issue altogether. The development of improved models for the Reynolds stress tensor via tensor-basis neural networks results in more predictive RANS models across a suite of low-speed and high-speed cases. For hypersonic boundary layers, the inclusion of the wall-normal Reynolds stress via TBNNs has an appreciable impact on the wall-normal momentum balance and wall quantities. However, we find that improvements to the Reynolds stress tensor do not address the over-prediction in wall heat flux in SBLIs. We find that a neural-network-based variable turbulent Prandtl number model systematically and substantially improves wall heating predictions for a range of SBLI cases.

97 MATHEMATICS AND COMPUTING↗