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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2023

Technical Specification Surveillance Interval Extension Using Self-Diagnostics

As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.

42 ENGINEERING↗
DOE OSTI2023

Structure, Function, and Application of Self–Healing Adhesives from Mistletoe Viscin

Berries from the European Mistletoe (Viscum album) possess a sticky tissue called viscin that facilitates adhesion and germination onto host trees. Recent studies of viscin have demonstrated its adhesive capacity on a range of natural and synthetic surfaces including wood, skin, metals, and plastic. Yet, the underlying mechanisms remain poorly understood. Here, an investigation of the adhesive performance of mistletoe viscin is performed, demonstrating its hygroscopic nature and ability to self-heal following adhesive failure. It is identified that adhesion originates from a water-soluble adhesive component that can be extracted, isolated, and characterized independently. Lap shear mechanical testing indicates that the mistletoe adhesive extract (MAE) outperforms native viscin tissue, as well as gum arabic and arabinogalactan—common plant-based adhesives. Furthermore, humidity uptake experiments reveal that MAE can reversibly absorb nearly 100% of its mass in water from the atmosphere. In-depth spectroscopic and mass spectrometry investigations reveal a composition consisting primarily of an atypical arabinogalactan, with additional sugar alcohols. Finally, several proof-of-concept applications are demonstrated using MAE for hygro-responsive reversible adhesion between various surfaces including skin, plastic, PDMS, and paper, revealing that MAE holds potential as a biorenewable and reusable adhesive for applications in cosmetics, packaging, and potentially, tissue engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimized matching conditions for self-guided laser wakefield accelerators

We revisit the matching conditions for self-guided laser pulse propagation in plasma and refine their formulation to maximize the energy of electrons produced via laser wakefield acceleration. Bayesian optimization, combined with particle-in-cell simulations carried out in a quasi-three-dimensional geometry and a Lorentz-boosted frame, is employed. The optimization identifies the maximum electron energy that a self-guided laser wakefield accelerator, driven by a laser of a given energy, can produce, together with the corresponding acceleration distance. Our results further demonstrate that electrons with energies close to the maximum value can be obtained across a relatively wide range of input parameters and without the need for their precise tuning. This provides substantial flexibility for experimental implementation and significantly relaxes the operational constraints associated with self-guided laser wakefield accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Resummation of QED radiative corrections in a strong constant crossed field

By considering radiative corrections of up to 3rd-loop order, Ritus and Narozhny conjectured that the proper expansion parameter for QED in a strong constant crossed field is $g=\alpha\chi^{2/3}$, where the dynamical quantum parameter $\chi=e\sqrt{-(Fp)^2}/m^3$ combines the particle momentum $p$ with the external field strength tensor $F$. Here we present and discuss the first non-perturbative result in this context, the resummed bubble-type polarization corrections to the electron self-energy in a constant crossed field. Our analysis confirms the relevance of the scaling parameter $g$ to the enhancement of bubble-type radiative corrections. This parameter actually represents the characteristic value of the ratio of the 1-loop polarization bubble to the photon virtuality. After an all-order resummation we identify and discuss two contributions to the self-energy with different formation regions and asymptotic behavior for $g\gg1$. Whereas the breakdown of perturbation theory occurs already for $g\gtrsim1$, the leading-order result remains dominant until the asymptotic regime $g\gg 1$ is reached. However, the latter is specific to processes like elastic scattering or photon emission and does not have to remain true for general higher-order QED processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Quantitative evaluations of subtropical westerly jet simulations over East Asia based on multiple CMIP5 and CMIP6 GCMs

As a salient feature of the Asian monsoon system, the East Asian subtropical westerly jet (EASWJ) exerts significant impacts on weather and climate changes in China and even throughout East Asia. In this paper, we applied a new self-adaptive algorithm to detect the EASWJ, identify its boundaries, and then represent its characteristics by defining three indices: the intensity index, meridional displacement index, and width index. Compared to the reanalysis data, we carried out a comprehensive, objective, and quantitative EASWJ evaluation using historical experiments from multiple global climate models (GCMs) in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6). The results show that the multimodel ensemble mean (MME) of both CMIP5 and CMIP6 can simulate the characteristics of winter EASWJ well. While for the other three seasons, the MME of both phase models underestimate the 200-hPa zonal wind (U200) strength in the jet coverage area and overestimate the U200 outside the jet area, such simulation results weaken the meridional shear of the wind field. The EASWJ simulations from the CMIP5 GCMs had no consistent intensity or location characteristic tendencies, and most CMIP5 GCMs tended to simulate relatively wide-coverage jets. In contrast, most CMIP6 GCMs are inclined to simulate significantly weaker, wider, and more-northward jets. Compared to the predecessors in CMIP5, about half of CMIP6 GCMs significantly minimized the jet intensity bias, but remarkable errors were still observed in their jet location and coverage representations. Furthermore, the comparative analysis performed by classifying models based on their evaluated simulation results suggested that the simulated performance of the meridional temperature gradient was important for capturing the EASWJ characteristics. Further in-depth study of the causes of model differences is warranted to improve simulation results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Signatures of enhanced spin-triplet superconductivity induced by interfacial properties

While spin-triplet pairing remains elusive in nature, there is a growing effort to realize proximity-induced equal-spin triplet superconductivity in junctions with magnetic regions or an applied magnetic field and common s-wave superconductors. To enhance such spin-triplet contribution, it is expected that junctions with a weak interfacial barrier and strong spin-orbit coupling are desirable. Intuitively, a weak interfacial barrier enables a robust proximity-induced superconductivity and strong spin-orbit coupling promotes spin mixing, converting spin-singlet into spin-triplet superconductivity. In contrast, we reveal a nonmonotonic spin-triplet contribution with the strength of the interfacial barrier and spin-orbit coupling. This picture is established by considering different signatures in conductance and superconducting correlations, as well as by performing self-consistent calculations. As a result, we identify a strongly enhanced spin-triplet superconductivity, realized for an intermediate strength of interfacial barrier and spin-orbit coupling. In junctions with magnetic regions, an enhanced spin-triplet superconductivity leads to a large magnetoanisotropy of conductance and superconducting correlations. Furthermore, this picture of an enhanced spin-triplet superconductivity is consistent with experiments demonstrating a huge increase in the conductance magnetoanisotropy, which we predict can be further enhanced at a finite bias.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Few-cycle self-compression of GW mid-IR pulses in an anti-resonant fiber in ambient air

We report self-compression of mid-IR optical pulses to the few-cycle regime in an anti-resonant hollow core fiber filled with ambient air. Transform-limited 120-fs duration input optical pulses with 160 μJ energy are compressed to 29.8 fs (< 3 cycles) with a compression efficiency of 75%, resulting in 2.6 GW peak power few-cycle pulses. In addition, we show that nonlinear compression is maintained when the fiber is coiled, allowing for a compact setup. Lastly, we elucidate the dynamics involved in this self-compression setup via numerical simulations and identify the critical role played by Raman shifting in molecular gases and ionization limitations.

Hettel, Will [University of Colorado & NIST, Bould↗
DOE OSTI2021

Hydropower Cyber-Physical Configurations

The U.S. Department of Energy’s Water Power Technologies Office funded Pacific Northwest National Laboratory, Argonne National Laboratory, and the National Renewable Energy Laboratory to develop a typology to characterize the variety and pervasiveness of cyber-physical configurations across the nation’s hydropower fleet. Outreach to owners and operators returned configurations for 275 hydropower plants or approximately 12% of the fleet. Components (OT and IT), systems, and connections among systems differed among plants according to function, age, position in the river cascade, and many other factors. Seven cyber-physical configuration types labeled A through I, included from 2 to dozens of plants. They were differentiated by how pervasive data and control connections were among cyber-physical components and how frequently control signals paired with data signals in a feedback loop. The flow of data and control within each type implies what cybersecurity vulnerabilities may exist, and what mitigation actions may be most effective. A self-assessment approach allows plant operators to identify the configuration type similar to their plant and link to the lessons learned and best practices information. The cyber-physical typology reinforces the idea that hydropower facilities vary widely, but it also identifies groups that highlight similarities in how their cyber-physical components interact. This helps address fleetwide cybersecurity needs by identifying a reasonable number of configuration types that share risks, vulnerabilities, and potential mitigations.

13 HYDRO ENERGY↗
DOE OSTI2025

The Future of X-ray Irradiation: Addressing Supply Chain Risks and Opportunities (UUR Edition)

This study supports the Office of Radiological Security’s (ORS) mission of eliminating cesium irradiators by analyzing the supply chain for self-shielded X-ray irradiators (SSXIs), identifying potential risks, and proposing mitigation measures. The research focuses on the primary components of SSXIs, including X-ray tubes, controllers, generators, and coolers or chillers, and evaluates their vulnerabilities using a comprehensive risk matrix framework. The methodology includes subject matter expert (SME) interviews with relevant manufacturers and major stakeholders, a deep literature review, and a meta-analysis of maintenance reports provided by SSXI end users. Results show that while the SSXI market is small, it’s growing, and the highly global nature of the supply chain may create vulnerabilities for critical SSXI components (X-ray tubes are the most vulnerable, followed by generators and controllers). This research communicates necessary information to address concerns of current and future end users, especially those interested in transitioning away from radioactive sources, and informs future policy aimed at supporting the irradiation industry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2026

The Complexation Properties of Self-Defensive Microgel-Modified Antimicrobial Surfaces

The complexation of cationic antimicrobials with polyanionic microgels on a biomaterial surface can render that surface self-defensive against bacteria by killing those bacteria which physically contact the antimicrobial-loaded microgels. This killing has been attributed to the contact-driven transfer of antimicrobial from a microgel to a challenging bacterium, though much remains unknown about this process. Here, in this study, we use a combination of experiments and computational modeling to identify key aspects of the complexation phenomena which influence the self-defensive properties. We synthesize poly(acrylic acid) (PAA) microgels (∼2–5 μm diameter) via membrane emulsification and electrostatically deposit them onto polycaprolactone (PCL) coupons or onto glass to form a discontinuous submonolayer. Subsequent microgel loading with colistin or with Sub5 antimicrobial peptide (AMP) causes microgel deswelling. Under physiological conditions Sub5 remains stably sequestered whereas colistin is quickly released. Coarse-grained molecular dynamics (CGMD) simulations confirm stronger Sub5/PAA complexation. CGMD calculations also indicate that Sub5 forms dimers and higher-order structures, a prediction confirmed experimentally by Small-Angle X-ray Scattering (SAXS). Supramolecular structure entropically enhances the complexation strength because of enhanced counterion release per complexation event, and this finding can help identify other antimicrobials well suited for such a nonelutive yet self-defensive strategy. CGMD simulations also show that Sub5 has a higher complexation strength with the Staphylococcus aureus membrane than it does with PAA, confirming that there is a thermodynamic driving force for antimicrobial transfer. Such self-defensive surfaces significantly reduce S. aureus colonization (over 90% reduction relative to unmodified controls) in an in vitro hematogenous contamination model and remain cyto-compatible as evidenced by mesenchymal stem cell spreading and proliferation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quarkonium Spectroscopy in the Quark-Gluon Plasma

The properties of bound states are fundamental to hadronic spectroscopy and play a central role in the transition from hadronic matter to a quark-gluon plasma (QGP). In a strongly coupled QGP (sQGP), the interplay of temperature, binding energy, and large collisional widths of the partons poses formidable challenges in evaluating the in-medium properties of hadronic states and their eventual melting. In particular, the existence of heavy quarkonia in the QGP is a long-standing problem that is hard to solve by considering their spectral properties on the real-energy axis. Here, we address this problem by analyzing in-medium thermodynamic quarkonium 𝑇 matrices in the complex energy plane. We first validate this method in vacuum, where the 𝑇-matrix poles of observed states are readily identified. When deploying this approach to recent self-consistently calculated 𝑇 matrices in the QGP, we find that poles in the complex energy plane can persist to surprisingly large temperatures, depending on the strength of the in-medium interactions. While the masses and widths of the pole positions are precisely defined, the notion of a binding energy is not due to the absence of thresholds caused by the (large) widths of the underlying quark or anti-quark spectral functions. Our method thus provides a new and definitive quantum-mechanical criterion to determine the melting temperature of hadronic states in the sQGP while increasing the accuracy in the theoretical determination of transport parameters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Northern Hemisphere Winter Air Temperature Patterns and Their Associated Atmospheric and Ocean Conditions

The Northern Hemisphere (NH) has experienced winter Arctic warming and continental cooling in recent decades, but the dominant patterns in winter surface air temperature (SAT) are not well understood. Here, a self-organizing map (SOM) analysis is performed to identify the leading patterns in winter daily SAT fields from 1979 to 2018, and their associated atmospheric and ocean conditions are also examined. Three distinct winter SAT patterns with two phases of nearly opposite signs and a time scale of 7–12 days are found: one pattern exhibits concurrent SAT anomalies of the same sign over North America (NA) and northern Eurasia, while the other two patterns show SAT anomalies of opposite signs between, respectively, NA and the Bering Sea, and the Kara Sea and East Asia (EA). Winter SAT variations may arise from changes in the SOM frequencies. Specifically, the observed increasing trends of winter cold extremes over NA, central Eurasia, and EA during 1998–2013 can be understood as a result of the increasing occurrences of some specific SAT patterns. These SOMs are closely related to poleward advection of midlatitude warm air and equatorward movements of polar cold airmass. These meridional displacements of cold and warm airmasses cause concurrent anomalies over different regions not only in SAT but also in water vapor and surface downward longwave radiation. Anomalous sea surface temperatures in the tropical Pacific, midlatitude North Pacific, and North Atlantic and anomalous Arctic sea ice concentrations also concur to support and maintain the anomalous atmospheric circulation that causes the SAT anomalies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Navigating United States Standards and Regulation for Digital Energy Systems

This report provides an analysis of the U.S. standards and regulatory landscape for digital energy systems, focusing on cybersecurity, safety, and reliability requirements. It examines the interplay between federal mandates, state regulations, voluntary industry standards, and utility-specific policies, highlighting critical gaps between compliance and real-world risk mitigation. While NERC CIP standards enforce cybersecurity for Bulk Electric System assets, distribution-level infrastructure and emerging technologies often fall outside mandatory oversight, creating vulnerabilities. The report identifies systemic challenges such as reliance on self-attestation, uneven state adoption of safety codes, and lagging standards for advanced technologies like battery energy storage and inverter-based resources. Through a detailed gap analysis, it underscores the urgency of proactive risk-based approaches, independent verification, and strategic engagement with state and federal entities. Recommendations include adopting tiered security frameworks, strengthening procurement practices, and addressing emerging technology risks to ensure resilient and secure digital energy infrastructure. This guidance is intended for utilities, regulators, and stakeholders navigating compliance obligations and seeking to enhance cybersecurity and safety beyond minimum standards.

24 - POWER TRANSMISSION AND DISTRIBUTION↗