SEARCH · Search NASA

Results for “simulation datasets”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,513 records · Page 84

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model

Abstract Background Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter‐reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case. Purpose This study aims to develop a deep learning‐based technique to generate multiple CTV contours for each case, simulating the inter‐reader variability in the clinical practice. Methods We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG‐PET), x‐ray computed tomography (CT), and pre‐contrast T1‐weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi‐modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model‐based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter‐reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation. Results The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state‐of‐the‐art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state‐of‐the‐art models). It also achieved the second‐highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation. Conclusions The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter‐reader variability in CTV delineation.

Dong, Yafei [Yale Biomedical Imaging Institute Yal↗

DOE OSTI2023

Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m –2 yr –1 ). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

A multi-dimensional dataset for two-phase instability in low pressure natural circulation based on direct transient local measurement

Thermal hydraulic instability can challenge the control and the safety of nuclear reactors. This experimental research captures and investigates the instability in a low-pressure (1–10 atmospheres) natural circulation loop with an adiabatic chimney installed above the heated section. Direct transient local measurement of two-phase flow is performed at multiple axial locations along the flow channel with thermocouples and conductivity probes traversed by customized high-precision mechanisms. Based on the periodicity of oscillations, a post-processing method is proposed to align the data captured non-simultaneously to reconstruct the instability behavior in one representative period. The averaging operation over multiple oscillation cycles is performed to reduce the random errors. Cyclic phenomena are clearly demonstrated by the post-processed data, and rapid void fraction change induced by the flashing is observed to be significant in the driving mechanism for low pressure oscillations. Parametric effects of inlet subcooling, heat flux, inlet flow restriction and system pressure are studied within the experiment range, and qualitative results are explained and compared with existing literature. The ratio between the oscillation period and the travelling time of the fluid particle through the test section is compared with the theoretical value for the density wave oscillation. The new dataset contains directly measured transient multi-dimensional two-phase data along the flow channel for natural circulation under density wave oscillations. The generated dataset is valuable for the validation of system analysis codes and CFD codes in the simulation of natural circulation phenomena and dynamic two-phase flow.

42 ENGINEERING↗

DOE OSTI2020

An investigation of irreproducibility in maximum likelihood phylogenetic inference

Phylogenetic trees are essential for studying biology, but their reproducibility under identical parameter settings remains unexplored. Here, we find that 3515 (18.11%) IQ-TREE-inferred and 1813 (9.34%) RAxML-NG-inferred maximum likelihood (ML) gene trees are topologically irreproducible when executing two replicates (Run1 and Run2) for each of 19,414 gene alignments in 15 animal, plant, and fungal phylogenomic datasets. Notably, coalescent-based ASTRAL species phylogenies inferred from Run1 and Run2 sets of individual gene trees are topologically irreproducible for 9/15 phylogenomic datasets, whereas concatenation-based phylogenies inferred twice from the same supermatrix are reproducible. Our simulations further show that irreproducible phylogenies are more likely to be incorrect than reproducible phylogenies. These results suggest that a considerable fraction of single-gene ML trees may be irreproducible. Increasing reproducibility in ML inference will benefit from providing analyses’ log files, which contain typically reported parameters (e.g., program, substitution model, number of tree searches) but also typically unreported ones (e.g., random starting seed number, number of threads, processor type).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Toward the performance assessment of advanced nuclear waste forms: temperature dependence of lanthanide borosilicate glass dissolution

Lanthanide borosilicate (LaBS) glasses are among the most promising waste forms for the immobilization of high-level radioactive waste generated from advanced nuclear fuel cycles. However, the temperature dependence of their dissolution kinetics remains poorly understood and constrained, limiting the integration of these materials into established performance assessment models. Here, we investigate the dissolution behavior of the legacy AmCm2-19 LaBS glass and the benchmark alkali aluminoborosilicate ISG-1 in deionized water between 50 °C and 250 °C using ASTM C1285 (Product Consistency Test-B) protocols. For AmCm2-19 LaBS glass, normalized elemental release rates for boron and silicon increase with temperature before plateauing near 150 °C, consistent with solubility-limited behavior. From data obtained at 50 °C and 100 °C, Arrhenius analysis yields activation energies of E a (B) = 24.8 ± 0.3 kJ mol⁻¹ and E a (Si) = 14.4 ± 0.2 kJ mol⁻¹, similar or slightly lower than those previously reported for two other compositions of LaBS glasses. No secondary phases or alteration layers were detected by SEM-EDX or pXRD. These results establish one of the first temperature-dependent kinetic datasets for LaBS glass dissolution, providing quantitative parameters to inform mechanistic corrosion models and predictive simulations of glass degradation in geological disposal environments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Laser-driven, ion-scale magnetospheres in laboratory plasmas. II. Particle-in-cell simulations

Ion-scale magnetospheres have been observed around comets, weakly magnetized asteroids, and localized regions on the Moon and provide a unique environment to study kinetic-scale plasma physics, in particular in the collision-less regime. In this work, we present the results of particle-in-cell simulations that replicate recent experiments on the large plasma device at the University of California, Los Angeles. Using high-repetition rate lasers, ion-scale magnetospheres were created to drive a plasma flow into a dipolar magnetic field embedded in a uniform background magnetic field. The simulations are employed to evolve idealized 2D configurations of the experiments, study highly resolved, volumetric datasets, and determine the magnetospheric structure, magnetopause location, and kinetic-scale structures of the plasma current distribution. We show the formation of a magnetic cavity and a magnetic compression in the magnetospheric region, and two main current structures in the dayside of the magnetic obstacle: the diamagnetic current, supported by the driver plasma flow, and the current associated with the magnetopause, supported by both the background and driver plasmas with some time-dependence. From multiple parameter scans, we show a reflection of the magnetic compression, bounded by the length of the driver plasma, and a higher separation of the main current structures for lower dipolar magnetic moments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Precision Measurement of Neutrino Oscillation Parameters with 10 Years of Data from the NOvA Experiment

This Letter reports measurements of muon-neutrino disappearance and electron-neutrino appearance and the corresponding antineutrino processes between the two NOvA detectors in the NuMI neutrino beam. These measurements use a dataset with double the neutrino mode beam exposure that was previously analyzed, along with improved simulation and analysis techniques. A joint fit to these samples in the three-flavor paradigm results in the most precise single-experiment constraint on the atmospheric neutrino mass-splitting, $Δm^2_{32}= 2.431^{+0.036}_{-0.034} (-2.479^{+0.036}_{-0.036}) \times 10^{-3}$~eV$^2$ if the mass ordering is Normal (Inverted). In both orderings, a region close to maximal mixing with $\sin^2θ_{23}=0.55_{+0.06}^{-0.02}$ is preferred. The NOvA data show a mild preference for the Normal mass ordering with a Bayes factor of 2.4 (corresponding to 70% of the posterior probability), indicating that the Normal ordering is 2.4 times more probable than the Inverted ordering. When incorporating a 2D $Δm^2_{32}\textrm{--}\sin^2 2θ_{13}$ constraint based on Daya Bay data, this preference strengthens to a Bayes factor of 6.6 (87\%).

FOS: Physical sciences↗

DOE OSTI2022

Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.

58 GEOSCIENCES↗

DOE OSTI2024

Discovering the Unknowns: A First Step

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

42 ENGINEERING↗

DOE OSTI2023

Skyrme–Hartree–Fock–Bogoliubov mass models on a 3D mesh: IIb. Fission properties of BSkG2

Large-scale models of nuclear structure are currently the only way to provide consistent datasets for the many properties of thousands of exotic nuclei that are required by nucleosynthesis simulations. In [W. Ryssens et al., Eur. Phys. J. A 58, 246 (2022)], we recently presented the new BSkG2 model based on an energy density functional of the Skyrme type. Relying on a flexible three-dimensional coordinate representation of the nucleus, the model takes into account both triaxial deformation and time-reversal symmetry breaking. BSkG2 achieves a state-of-the-art global description of nuclear ground state (g.s.) properties and reproduces in particular the known masses with a root-mean-square (rms) deviation of 678 keV. Moving beyond g.s. properties, the model also reproduces all empirical values for the primary and secondary barriers as well as isomer excitation energies of actinide nuclei with rms deviations below 500 keV, i.e. with unprecedented accuracy. Here we discuss in detail the extension of our framework to the calculation of the fission barriers of 45 actinide nuclei, including odd-mass and odd-odd systems. We focus in particular on the impact of symmetry breaking which is key to the accuracy of the model: we allow systematically for axial, reflection and time-reversal symmetry breaking. The effect of the latter on the fission properties of odd-mass and odd-odd nuclei is small, but we find that allowing for shapes with triaxial or octupole deformation, as well as shapes with both, is crucial to achieving this accuracy. Here, the numerical accuracy of our coordinate space approach, the variety of nuclear configurations explored and the simultaneous successful description of fission properties and known masses makes BSkG2 the tool of choice for the large-scale study of nuclear structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

AESD (Advanced Energy System Design) Records API [SWRs-17-50, 17-51, 17-52]

The Records API (application program interface) for Advanced Energy System Design (AESD) enables software that serves multidimensional record-oriented data to interoperate with software than uses such data. In the context of the Records API, multidimensional data records are simply tuples of real numbers, integers, and character strings, where each data value is tagged by a variable name, according to a pre-defined schema, and each record is assigned a unique integer identifier. Conceptually, these records are isomorphic to rows in a relational database, JSON objects, or key-value maps. Records servers might supply static datasets, sensor measurements that periodically update as new telemetry become available, or the results of simulations as the simulations generate new output. Records client software might display or analyze the data, but in the case of simulations the client request the creation of new ensembles for specified input parameters. It is also possible to chain records clients and servers together so that a client consuming data from a server might transform that data and serve it to additional clients. This minimalist API avoids imposing burdensome metadata, structural, or implementation requirements on developers by relying on open-source technologies that are readily available for common programming languages. In particular, the API has been designed to place the least possible burden on services that provide data. This document defines the message format for the Records API, a transport mechanism for communicating the data, and the semantics for interpreting it. The message format is specified as Google Protocol Buffers and the transport mechanism uses WebSockets. We discuss three major use cases for serving and consuming records data: (i) static data, (ii) dynamically augmented data, (iii) on-demand simulations, (iv) with filters, and (v) with bookmarks. Separate implementations of the API exist in C++, Haskell, JavaScript, Python, and R.

Bush, Brian↗

DOE OSTI2026

HITMAN

HITMAN (Hermite Interpolation of Trajectories and Measurement Synthesis for Analysis of Navigators) interpolates—or estimates the unknown values between known values—flight trajectories and generates synthetic inertial measurement unit (IMU) data using Hermite splines. This Python library provides modeling and simulation capabilities to synthesize inertial measurements from discrete trajectory points, enabling researchers to create exemplar datasets for evaluating navigation algorithms in various applications, including consumer devices like smartphones and vehicles. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗

DOE OSTI2019

A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Interannual Tropospheric Aerosol Variability in the Late Twentieth Century and Its Impact on Tropical Atlantic and West African Climate by Direct and Semidirect Effects

A new high-resolution global tropospheric aerosol dataset with monthly resolution is created using version 4 of the Community Atmosphere Model (CAM4) coupled to a bulk aerosol model and forced with recent estimates of surface emissions for the period 1961–2000 to identify tropospheric aerosol-induced interannual climate variations. Here, the surface emissions dataset is constructed from phase 5 of the Coupled Model Intercomparison Project (CMIP5) decadal-resolution surface emissions dataset to include reanalysis of tropospheric chemical composition [40-yr Reanalysis of Tropospheric Chemical Composition (RETRO)] wildfire monthly emissions data. A four-member ensemble run is conducted using the spectral configuration of CAM4, forced with the new tropospheric aerosol dataset and prescribed with observed sea surface temperature, sea ice, and greenhouse gases. CAM4 only simulates the direct and semidirect effects of aerosols on the climate. The simulations reveal that variations in tropospheric aerosol levels can induce significant regional climate variability on the interannual time scales. Regression analyses over tropical Atlantic and Africa suggest that increasing dust aerosols can cool the North African landmass and shift convection southward from West Africa into the Gulf of Guinea in the spring season. Moreover, it is found that carbonaceous aerosols emanating from the southwestern African savannas can significantly cool the region and increase the marine stratocumulus cloud cover over the southeast tropical Atlantic Ocean by aerosol-induced diabatic heating of the free troposphere above the low clouds. Experiments conducted with CAM4 coupled to a slab ocean model suggest that present-day aerosols can cool the tropical North Atlantic and shift the intertropical convergence zone southward and can reduce the ocean mixed layer temperature beneath the increased marine stratocumulus clouds in the southeastern tropical Atlantic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Model Scripts for "Old-Aged Groundwater Contributes to Mountain Hillslope Hydrologic Dynamics"

The partitioning of water inputs between deep and shallow groundwater flow paths is a fundamental processes, yet is challenging to observe. Numerical models provide a valuable tool to further develop insights on these groundwater mixing processes. This repository contains scripts to run the ParFlow-CLM and EcoSLIM integrated hydrologic models along the Pumphouse Hillslope in the East River Watershed and associated python scripts to process model outputs. Files includes input decks for the associated models, python scripts, and text and css files to support the model runs and processing. The 2-D hillslope model simulates from 2000 to 2021 using transient forcing conditions from the publicly available NLDAS-2 dataset. We develop an ensemble of models with variable hydrogeologic and soil parameters. Model outputs from the ensemble of runs are compared to to water level observations at the PLM1 and PLM6 wells published at (https://data.ess-dive.lbl.gov/view/doi:10.15485/1866836) and groundwater mean ages published at (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042. The model results are used to evaluate hillslope water mass-balance transience and mixing dynamics between groundwater flow paths with young (<10 year) and old (>10 year) ages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

NETL Electrical Conductivity Relaxation(ECR) Analysis Tool

This application was developed within the solid oxide fuel cell (SOFC) work plan. It calculates the oxygen surface exchange coefficient (k) and oxygen diffusion coefficient (D) for an oxide sample using electrical conductivity relaxation (ECR) data. The tool also produces a heat map showing the associated uncertainty in fitting the ECR data for given pairs of k and D values, so the user can determine the most likely range of k and D values for the tested values. Multiple datasets for the same material can be plotted to decrease uncertainty. The tool can also simulate ECR curves for inputted k and D values and overlay the simulated curves with experimental data for comparison.

electrical conductivity relaxation↗

DOE OSTI2018

Advanced Energy System Design (AESD): Technical Manual for the Records API

The Records API (application program interface) for Advanced Energy System Design (AESD) enables software that serves multidimensional record-oriented data to interoperate with software than uses such data. In the context of the Records API, multidimensional data records are simply tuples of real numbers, integers, and character strings, where each data value is tagged by a variable name, according to a pre-defined schema, and each record is assigned a unique integer identifier. Conceptually, these records are isomorphic to rows in a relational database, JSON objects, or key-value maps. Records servers might supply static datasets, sensor measurements that periodically update as new telemetry become available, or the results of simulations as the simulations generate new output. Records client software might display or analyze the data, but in the case of simulations the client request the creation of new ensembles for specified input parameters. It is also possible to chain records clients and servers together so that a client consuming data from a server might transform that data and serve it to further clients. This minimalist API avoids imposing burdensome metadata, structural, or implementation requirements on developers by relying on open-source technologies that is readily available for common programming languages. In particular, the API has been designed to place the smallest possible burden on services that provide data. This document defines the message format for the Records API, a transport mechanism for communicating the data, and semantics for interpreting it. The message format is specified as Google Protocol Buffers and the transport mechanism uses WebSockets. We discuss three major use cases for serving and consuming records data: (i) static data, (ii) dynamically augmented data, (iii) on-demand simulations, (iv) with filters, and (v) with bookmarks. Separate implementations of the API exist in C++, Haskell, JavaScript, Python, and R.

97 MATHEMATICS AND COMPUTING↗