Search NASA⌕ Search

SEARCH · Search NASA

Results for “Measurement error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

STEPs-SOL, a Peptoid Force Field Parameterization to Include Solvent Effects

As peptoids (N-substituted glycines) continue to gain popularity as a class of biomimetic polymers, the importance and demand for accurate force fields in molecular simulations also grow. Building on the vacuum-optimized Systematic and Extensible Force Field for Peptoids (STEPs) force field, here we present STEPs-SOL, a novel peptoid force field parametrization that effectively incorporates solvent effects to enhance the accuracy of peptoid simulations. The development of STEPs-SOL is based on the need for precise electrostatic modeling achieved through solvent-specific partial charge optimization. Here, our systematic approach significantly improves agreement with experimental measurements, reducing the mean absolute error in cis/trans ratio predictions (ΔG c/t ) by an average of 38% across multiple peptoid residues and solvent environments. This improved parametrization addresses computational challenges associated with nonbonded energies while maintaining a workflow that relies on high-level quantum mechanical data rather than depending solely on limited experimental equilibrium properties. By evaluating the effects of conformational bias in restrained electrostatic potential (RESP) charge generation and examining their impact on peptoid conformations in various solvents, we enhance our understanding of peptoid structural dynamics while providing a more accurate modeling framework.

force field↗
DOE OSTI2023

Galactic ArchaeoLogIcaL ExcavatiOns (GALILEO) II. t-SNE portrait of local fossil relics and structures

Based on high-quality Apache Point Observatory Galactic Evolution Experiment (APOGEE) DR17 and Gaia DR3 data for 1742 red giants stars within 5 kpc of the Sun and not rotating with the Galactic disk (V φ < 100 km s -1 ), we used the nonlinear technique of unsupervised analysis t-Distributed Stochastic Neighbor Embedding (t-SNE) to detect coherent structures in the space of ten chemical-abundance ratios: [Fe/H], [O/Fe], [Mg/Fe], [Si/Fe], [Ca/Fe], [C/Fe], [N/Fe], [Al/Fe], [Mn/Fe], and [Ni/Fe]. Additionally, we obtained orbital parameters for each star using the nonaxisymmetric gravitational potential GravPot16. Seven structures are detected, including Splash, Gaia-Sausage-Enceladus (GSE), the high-α heated-disk population, N-C-O peculiar stars, and inner disk-like stars, plus two other groups that did not match anything previously reported in the literature, here named Galileo 5 and Galileo 6 (G5 and G6). These two groups overlap with Splash in [Fe/H], with G5 having a lower metallicity than G6, and they are both between GSE and Splash in the [Mg/Mn] versus [Al/Fe] plane, with G5 being in the α-rich in situ locus and G6 on the border of the α-poor in situ one. Nonetheless, their low [Ni/Fe] hints at a possible ex situ origin. Their orbital energy distributions are between Splash and GSE, with G5 being slightly more energetic than G6. We verified the robustness of all the obtained groups by exploring a large range of t-SNE parameters, applying it to various subsets of data, and also measuring the effect of abundance errors through Monte Carlo tests.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13 C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was 169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Introducing a multiscale feature integration network for inpainting with applications to enhanced CMB map reconstruction

We introduce a novel neural network, SkyReconNet, which combines the expanded receptive fields of dilated convolutional layers along with standard convolutions, to capture both the global and local features for reconstructing the missing information in an image. We implement our network to inpaint the masked regions in a full-sky cosmic microwave background (CMB) map. Inpainting CMB maps is a particularly formidable challenge when dealing with extensive and irregular masks, such as galactic masks which can obscure substantial fractions of the sky. The hybrid design of SkyReconNet leverages the strengths of standard and dilated convolutions to accurately predict CMB fluctuations in the masked regions by effectively utilizing the information from surrounding unmasked areas. During training, the network optimizes its weights by minimizing a composite loss function that combines the structural similarity index measure (SSIM) and mean squared error (MSE). SSIM preserves the essential structural features of the CMB, ensuring an accurate and coherent reconstruction of the missing CMB fluctuations, while MSE minimizes the pixelwise deviations, thus enhancing the overall accuracy of the predictions. The predicted CMB maps and their corresponding angular power spectra align closely with the targets, achieving the performance limited only by the fundamental uncertainty of cosmic variance. The network’s generic architecture enables application to other physics-based challenges involving data with missing or defective pixels, systematic artifacts, etc. In conclusion, our results demonstrate its effectiveness in addressing the challenges posed by large irregular masks, offering a significant inpainting tool not only for CMB analyses but also for image-based experiments across disciplines where such data imperfections are prevalent.

Cosmic microwave background↗
DOE OSTI2024

Performance of the first mechanically-coupled twisting heliostat: encircled energy vs angle of incidence, and closed loop tracking

The first automatic twisting heliostat, with 8 m2 reflector, was completed and tested on-sun in January 2024. It was set up on a target-oriented dual-axis mount, with the target-axis aimed at a target 113 m to the west. The shape-twisting is purely automatic, made by a mechanical cam coupling to the cross-axis, which turns as the angle of incidence of the sun’s rays on the reflector. Three unsaturated images taken at different times of day were recorded of the sun on the target, reflected at angles of incidence of 5°, 49° and 68°. The measured FWHM of three images is very similar, about 1.09 m or 9.5 mrad, only slightly larger than an ideal solar disc, indicating that the different twisted reflector shapes are close to the ideal biconics needed to image the sun. The encircled energy measured for all three images was similar, 87% within approximately 1.30 m diameter. We report here also on a novel, tracking camera that employs a semitransparent beamsplitter fixed perpendicular to the mirror surface and to the plane of incidence. Over 2.5 hours of measurement of closed loop tracking, errors were ≤ 0.36 mrad rms, in both tracking axes.

14 SOLAR ENERGY↗
DOE OSTI2024

Entanglement-based quantum digital signatures over a deployed campus network

The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today’s digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Combined speckle- and propagation-based single shot two-dimensional phase retrieval method

Single-shot two-dimensional (2D) phase retrieval (PR) can recover the phase shift distribution within an object from a single 2D x-ray phase contrast image (XPCI). Two competing XPCI imaging modalities often used for single-shot 2D PR to recover material properties critical for predictive performance capabilities are: speckle-based (SP-XPCI) and propagation-based (PB-XPCI) XPCI imaging. However, PR from SP-XPCI and PB-XPCI images are, respectively, limited to reconstructing accurately slowly and rapidly varying features due to noise and differences in their contrast mechanisms. Herein, we consider a combined speckle- and propagation-based XPCI (SPB-XPCI) image by introducing a mask to generate a reference pattern and imaging in the near-to-holographic regime to induce intensity modulations in the image. We develop a single-shot 2D PR method for SPB-XPCI images of pure phase objects without imposing restrictions such as object support constraints. It is compared against PR methods inspired by those developed for SP-XPCI and PB-XPCI on simulated and experimental images of a thin glass shell before and during shockwave compression. Reconstructed phase maps show improvements in quantitative scores of root-mean-square error and structural similarity index measure using our proposed method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Micrometer-accuracy 2D displacement interferometer with plasmonic metasurface resonators

In this Letter, a high-accuracy, two-dimensional displacement sensor is proposed, designed, and demonstrated based on the concept of an extrinsic Fabry–Perot Interferometer. The sensor is composed of two bundled single-mode optic fibers in parallel and two plasmonic metasurface resonators inscribed on a gold substrate via a focused ion beam. The fiber end surface and the metasurface are in parallel with a small cavity between. The cavity change or Z -component displacement is determined from the pattern of interference fringes. The X -component displacement, perpendicular to the Z component, is identified from wavelength-selective metasurface resonators, which possess unique resonant wavelengths due to different nanostructure designs. The sensor was calibrated with six displacements applied through a three-axis precision linear stage. Test results indicated that the proposed interferometer can measure displacements with a maximum error of 5.4 µm or 2.2%.

Guo, Chuanrui (ORCID:0000000271412972)↗
DOE OSTI2025

Ultracoherent SRF Cavity-Based Multi-Qudit Platform with Error-Resilient Control

Superconducting radio-frequency (SRF) cavities offer a promising platform for quantum computing due to their long coherence times, yet integrating nonlinear elements like transmons for control often introduces additional loss. We report a multimode quantum system based on a 2-cell elliptical shaped SRF cavity, comprising two cavity modes weakly coupled to an ancillary transmon circuit, designed to preserve coherence while enabling efficient control of the cavity modes. We mitigate the detrimental effects of the transmon decoherence through careful design optimization that reduces transmon-cavity couplings and participation in the dielectric substrate and lossy interfaces, to achieve single-photon lifetimes of 20.6\,ms and 15.6\,ms for the two modes, and a pure dephasing time exceeding 40\,ms. This marks an order-of-magnitude improvement over prior 3D multimode memories. Leveraging sideband interactions and novel error-resilient protocols, including measurement-based correction and post-selection, we achieve high-fidelity control over quantum states. This enables the preparation of Fock states up to $N = 20$ with fidelities exceeding 95\%, the highest reported to date to the authors' knowledge, as well as two-mode entanglement with an estimated coherence-limited fidelities of 99.9\% after post-selection. These results establish our platform as a robust foundation for quantum information processing, allowing for future extensions to high-dimensional qudit encodings.

Kim, T. [Northwestern U.]↗
DOE OSTI2025

Pushing the Boundaries of Coherence in Superconducting Quantum Systems at the SQMS Center for Computing, Sensing, and Metrology

This talk will highlight recent advances at the SQMS Center in understanding and mitigating decoherence in superconducting quantum systems, focusing on both transmon qubits and 3D superconducting cavities. I will present results from systematic studies of materials and devices, aimed at identifying key sources of loss: including two-level systems (TLS), quasiparticles, and other noise mechanisms. These studies span microwave loss characterization in materials such as niobium, tantalum, aluminum, and their native oxides, as well as substrate losses in silicon and sapphire. Using both qubits and cavities, we disentangle subsystem contributions to loss and develop a hierarchy of mitigation strategies. Through these efforts, we have achieved transmon coherence times exceeding one millisecond. I will also present investigations into quasiparticle dynamics, including bursts observed in qubits located above ground and in the Gran Sasso underground laboratory. Additional studies explore temperature dependence to distinguish between TLS and quasiparticle losses, and reveal that applying a magnetic field can reduce temporal T1 fluctuations in transmons. Building on these results, we demonstrate a record-coherence two-cell cavity-qudit system with coherence times exceeding 20 milliseconds. By leveraging sideband interactions and error-resilient protocols including measurement-based correction and post-selection, we achieve high-fidelity quantum state control. This includes preparation of Fock states up to N = 20 with fidelities over 95%, and two-mode entanglement with coherence-limited fidelities reaching 99.9% after post-selection. These achievements position the SQMS platform as a powerful foundation for scalable quantum information processing and high-dimensional qudit encodings. Finally, I will discuss how these ultra-coherent systems are being deployed in emerging quantum sensing applications, including dark matter searches and gravitational wave detection.

Roy, Tanay [Fermilab]↗
DOE OSTI2026

Ultracoherent superconducting cavity-based multiqudit platform with error-resilient control

Superconducting radio-frequency (SRF) cavities offer a promising platform for quantum computing due to their long coherence times, yet integrating nonlinear elements like transmons for control often introduces additional loss. We report a multimode quantum system based on a 2-cell elliptical-shaped SRF cavity, comprising two cavity modes weakly coupled to an ancillary transmon circuit, designed to preserve coherence while enabling efficient control of the cavity modes. We mitigate the detrimental effects of the transmon decoherence through careful design optimization that reduces transmon-cavity couplings and participation in the dielectric substrate and lossy interfaces, to achieve single-photon lifetimes of 20.6 ms and 15.6 ms for the two modes, and a pure dephasing time exceeding 40 ms. This marks an order-of-magnitude improvement over prior 3D multimode memories. Leveraging sideband interactions and novel error-resilient protocols, including measurement-based correction and post-selection, we achieve high-fidelity control over quantum states. This enables the preparation of Fock states up to N = 20 with fidelities exceeding 95%, the highest reported to date to the authors' knowledge, as well as two-mode entanglement with an estimated coherence-limited fidelities of 99.9% after post-selection. These results establish our platform as a robust foundation for quantum information processing, allowing for future extensions to high-dimensional qudit encodings.

Lu, Yao [Fermilab] (ORCID:000000020413698X)↗
DOE OSTI2026

Ultracoherent superconducting cavity-based multiqudit platform with error-resilient control

Superconducting radio-frequency (SRF) cavities offer a promising platform for quantum computing due to their long coherence times, yet integrating nonlinear elements like transmons for control often introduces additional loss. We report a multimode quantum system based on a 2-cell elliptical-shaped SRF cavity, comprising two cavity modes weakly coupled to an ancillary transmon circuit, designed to preserve coherence while enabling efficient control of the cavity modes. We mitigate the detrimental effects of the transmon decoherence through careful design optimization that reduces transmon-cavity couplings and participation in the dielectric substrate and lossy interfaces, to achieve single-photon lifetimes of 20.6 ms and 15.6 ms for the two modes, and a pure dephasing time exceeding 40 ms. This marks an order-of-magnitude improvement over prior 3D multimode memories. Leveraging sideband interactions and novel error-resilient protocols, including measurement-based correction and post-selection, we achieve high-fidelity control over quantum states. This enables the preparation of Fock states up to N = 20 with fidelities exceeding 95%, the highest reported to date to the authors' knowledge, as well as two-mode entanglement with an estimated coherence-limited fidelities of 99.9% after post-selection. These results establish our platform as a robust foundation for quantum information processing, allowing for future extensions to high-dimensional qudit encodings.

Lu, Yao [Fermilab] (ORCID:000000020413698X)↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

An Empirical Study on the Use of the Rancor Microworld Simulator to Support Full-scope Data Collection

A lack of data has been identified as a major challenge in human reliability analysis (HRA). Accordingly, several institutes and researchers have tried to collect HRA data from different data sources such as actual historical measurements, expert judgements, or simulator studies. While the most recent studies predominantly focus on collecting data using full-scope simulators with actual operators, Idaho National Laboratory (INL) has begun to collect HRA data using a simplified simulator, i.e., the Rancor Microworld simulator, with student participants. Full-scope studies have been known to have several intrinsic challenges to securing enough quantity of the data due to many reasons like the high cost for performing experiments or requiring actual operators’ cooperation. The ultimate goal of INL’s effort aims to infer actual operators’ data collected from a full-scope simulator on the basis of microworld data with student subjects as well as collect additional data that could be missed in the full-scope research. As a first step to achieve this goal, this paper projects an experimental plan for investigating the differences in human performance between individuals in two groups: 1) an actual operator and 2) a student when using the Rancor Microworld simulator. A randomized factorial experiment design has been developed with two independent variables, i.e., type of scenario and type of subject. Six human performance measures, i.e., 1) time, 2) error, 3) workload, 4) situation awareness, 5) patterns of attention and 6) the number of manipulations were selected. A couple of scenarios and their procedures available to the Rancor Microworld simulator have been developed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Ultracoherent superconducting cavity-based multiqudit platform with error-resilient control

Superconducting radio-frequency (SRF) cavities offer a promising platform for quantum computing due to their long coherence times, yet integrating nonlinear elements like transmons for control often introduces additional loss. We report a multimode quantum system based on a 2-cell elliptical shaped SRF cavity, comprising two cavity modes weakly coupled to an ancillary transmon circuit, designed to preserve coherence while enabling efficient control of the cavity modes. We mitigate the detrimental effects of the transmon decoherence through careful design optimization that reduces transmon-cavity couplings and participation in the dielectric substrate and lossy interfaces, to achieve single-photon lifetimes of 20.6 ms and 15.6 ms for the two modes, and a pure dephasing time exceeding 40 ms. This marks an order-of-magnitude improvement over prior 3D multimode memories. Leveraging sideband interactions and novel error-resilient protocols, including measurement-based correction and post-selection, we achieve high-fidelity control over quantum states. This enables the preparation of Fock states up to $N = 20$ with fidelities exceeding 95%, the highest reported to date to the authors' knowledge, as well as two-mode entanglement with an estimated coherence-limited fidelities of 99.9% after post-selection. These results establish our platform as a robust foundation for quantum information processing, allowing for future extensions to high-dimensional qudit encodings.

Kim, Taeyoon [Fermilab; Northwestern U.]↗
DOE OSTI2022

Modeling land use and land cover change: using a hindcast to estimate economic parameters in gcamland v2.0

Abstract. Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. Estimates of future land use and land cover differ significantly across economic models as a result of differences in drivers, model structure, and model parameters; however, these models often rely on heuristics to determine model parameters. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. Specifically, we generate a large set of model parameter perturbations for the selected parameters and run gcamland simulations with these parameter sets over the historical period in the United States to quantify land use and land cover, determine how well the model reproduces observations, and identify parameter combinations that best replicate observations, assuming other model parameters are fixed. We also test alternate methods for forming expectations about uncertain crop yields and prices, including adaptive, perfect, linear, and hybrid approaches. In particular, we estimate parameters for six parameters used in the formation of expectations and three of seven logit exponents for the USA only. We find that an adaptive expectation approach minimizes the error between simulated outputs and observations, with parameters that suggest that for most crops, landowners put a significant weight on previous information. Interestingly, for corn, where ethanol policies have led to a rapid growth in demand, the resulting parameters show that a larger weight is placed on more recent information. We examine the change in model parameters as the metric of model error changes, finding that the measure of model fitness affects the choice of parameter sets. Finally, we discuss how the methodology and results used in this study could be used for other regions or economic models to improve projections of future land use and land cover change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Worldwide benchmarking of cost-effective radiometers for direct and diffuse irradiance

Solar energy projects can benefit from direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) measurements during all project phases. Several commercial measurement systems for DNI and DHI are available. Sun trackers with pyranometers and pyrheliometers can provide highly accurate measurements but are often impractical in solar energy applications. For less expensive and more robust sensors, it is often unclear which accuracy can be expected under a project site's specific atmospheric conditions. We address this challenge through our dedicated experimental comparison of relevant sensor systems (rotating shadowband irradiometer [short RSI], Delta-T SPN1, EKO MS-90, PyranoCam, Sunto CaptPro, Kipp & Zonen CSD3) at up to six sites worldwide. The RSI systems (rRMSD 3 to 8.6%, DNI; 4.8 to 7.6%, DHI) and PyranoCam (rRMSD 2.6 to 5.2%, DNI; 4.4 to 5.8%, DHI) exhibit similar error metrics and are the most accurate systems in the test. Delta-T SPN1 and EKO MS-90 (rRMSD 6.8 to 15%, DNI; 10.6 to 20.1%, DHI) but especially Kipp & Zonen CSD3 and Sunto CaptPro show significant deviations (rRMSD 17.7 to 20%, DNI; 33 to 58%, DHI). We evaluate the influence of relevant atmospheric parameters on the sensors' accuracies by a rather unique measurement setup. MS-90's DNI errors depend on DNI itself, with overestimations for low reference DNI. The deviations of SPN1's DHI and DNI measurements increase sharply in situations with high circumsolar irradiance. Also CaptPro and CSD3's increased measurement errors are related to circumsolar irradiance. For RSI and PyranoCam, only moderate influences on the measurements are identified, indicating a general applicability of these instruments.

14 SOLAR ENERGY↗