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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Free Energy Landscapes for Elucidating the Structural Consequences of Exon-20 mutations on the ErbB Family of Protein Kinases

The ErbB family of protein kinases plays an important role in major cellular functions and consequently mutations in the functional regions of these proteins are implicated in several types of cancer growths. To envision rational design of small molecule drugs that target the diseased proteins it is important to quantify the structural effects of the mutations, as some of these mutants render the protein resistant to tyrosine kinase inhibitors (TKIs). Herein we use advanced sampling techniques and long-timescale molecular dynamics simulations to predict the effect of major exon 20 mutations on the ErbB family, specifically EGFR and HER2 proteins. Exon 20 mutations have been clinically known to induce TKI resistance, though the mechanisms of such an effect is poorly understood. By mapping out the free energy landscape of the mutants and comparing them against the wild-type, we elucidate the structural differences in the binding pocket region that alter the nature of drug-protein interactions. We believe that these insights will play a pivotal role in developing small molecule drugs that overcome the TKI resistance.

Ashwin Ravichandran↗
NASA NTRS2019

Space Weathering Maps of (101955) Bennu Using a Radiative Transfer Model

Space weathering is an important process that affects the surfaces of airless bodies, such as (101955) Bennu. The consequences of this process include physical and chemical changes to materials on the surface, which in turn change spectral characteristics, especially in the visible to near infrared wavelengths. These spectral changes are not the same across airless bodies because the changes are dependent on the composition and mineralogy of the surface and even location within the Solar System. The main space weathering products responsible for these spectral changes are submicroscopic particles, which consist of two types, nanophase and microphase particles, and affect visible to near-infrared reflectance spectra differently. Nanophase particles are particles <33 nm in size and occur in agglutinates and within glassy patinas around regolith particles. In contrast, microphase particles are >33 nm in size and are present only within agglutinates. These spectral differences are best illustrated by lunar samples. In lunar soils, the nanophase and microphase particles consist of metallic iron. With increasing abundance of nanophase iron particles in a regolith, its spectrum exhibits a lower overall reflectance in the visible to near infrared, weakened absorption bands, and a reddened continuum slope. In contrast, an increasing abundance of microphase iron only causes decreases in reflectance and not reddening. Because of the spectral differences introduced by these two types of particles, it is possible to model the nanophase and microphase particle abundances of a surface through the radiative transfer technique. Beyond the Moon, the composition of the nanophase and microphase particles can include other phases because the mineralogy of the surfaces of other planetary bodies is different. For example, the nanophase and microphase particles may consist of amorphous carbon (Mercury) and sulfides (Itokawa). The mineralogy of Bennu is consistent with carbonaceous chondrites. From a number of space weathering experiments on CM chondrites, the likely nanophase and microphase mineral phases on Bennu includes iron, magnetite, and sulfides (i.e., pentlandite and troilite). The goal of this work is to input the predicted nanophase and microphase compositions for Bennu into the radiative transfer technique. Next, we use this technique to model the OSIRIS-REx Visible Infrared Spectrometer (OVIRS) so that we can model the nanophase and microphase particle abundances across the surface. This will result in space weathering maps of the surface of Bennu, which are useful for understanding the degree of space weathering across the surface and its relationship to various regions and geological features.

Trang, D.↗
NASA NTRS2019

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2). Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t/ha). This result highlights the synergy between 3D and multispectral data in AGB modelling. Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.

space-borne imagery↗
DOE OSTI2021

A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification

This paper describes a physics-guided Bayesian framework for identifying the milling stability boundary and system parameters through iterative testing. Prior uncertainties for the parameters are identified through physical simulation and literature reviews, without physical testing of the actual milling system. Those uncertainties are then propagated to the stability map using a physics-based stability model, which is used to suggest a test point. The uncertainties are updated based on the new information acquired from the cutting test to form a new probability distribution, called the posterior. Finally, the posterior are compared to measured values for the stability boundary and system parameters to evaluate the approach. Based on experimental observations, the advantages and disadvantages of using a physics-guided model are discussed.

Cornelius, Aaron↗
DOE OSTI2026

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗
DOE OSTI2025

Bias‐Dependent Quantum Efficiency Reveals Recombination Pathways in Thin Film Solar Cells

Identifying where recombination predominantly occurs—whether at the front interface, back interface, or throughout the bulk—is crucial for optimizing CdSeTe solar cells and many other photovoltaic device architectures. Here, a simple and effective diagnostic is demonstrated: measuring external quantum efficiency (QE) under varying forward biases. The drift–diffusion simulations reveal that each recombination pathway leaves a distinct bias-induced signature in the normalized QE: a progressive drop at long wavelengths for back-limited devices, a short-wavelength decline for front-limited devices, and a relatively uniform decrease across all wavelengths for bulk-limited devices. These predictions are validated with experiments on As-doped and Cu-doped CdSeTe devices, with and without passivation layers or different front buffers. In each case, the observed bias-dependent QE spectral changes align with the simulated recombination map. Because this method uses standard QE instrumentation, it offers a broadly accessible and cost-efficient means to diagnose recombination pathways—extending beyond CdSeTe to other thin-film and emerging solar cell technologies. By pinpointing recombination bottlenecks, researchers and manufacturers can strategically refine doping profiles, passivation schemes, and interface designs to further improve device performance.

CdTe↗
DOE OSTI2026

Computational Design of Interlayers for Thermally Stable Compositionally Graded Coatings on Nickel Alloys

To extend the service life of Ni-based superalloys, refractory metal coatings are often used. However, direct bonding between metals with dissimilar crystal structure promotes brittle intermetallic phase formation. This work presents a computational thermodynamic framework for high throughput design of functionally graded interlayers to suppress deleterious phases that may form at the interlayer. The Thermo-Calc software package was used to screen candidate metallic interlayer elements based on stability of solid-solution phases. Vanadium was identified as a promising interlayer due to its consistent suppression of intermetallic phases. Temperature-dependent phase diagram mapping between 600 and 1000 °C guided selection of a compositional pathway that significantly reduced intermetallic formation compared to directly joining the Ni-based and Nb refractory alloys. Time–temperature–transformation analysis was performed to assess whether equilibrium-predicted phases are kinetically accessible along regions of the graded path where non-solid-solution phases are not fully suppressed. The methodology was further applied to additional Ni-based alloy and coating systems, illustrating its transferability as an approach for rapid computational design of graded interlayers in dissimilar high-temperature materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
DOE OSTI2024

Electrical Resistivity Changes During Heating Experiments Unravel Heterogeneous Thermal‐Hydrological‐Mechanical Processes in Salt Formations

Abstract Rock salt is considered a suitable medium for the permanent disposal of heat‐generating radioactive waste due to its isolation properties. However, excavation damage and heating induce complex and heterogeneous thermal‐hydrological‐mechanical (THM) processes across different zones. Quantifying this heterogeneity is crucial for accurate long‐term performance assessment models, but traditional methods lack the necessary resolution. This study employs 4D electrical resistivity tomography (ERT) monitoring during controlled heating experiments in a salt formation to unravel the spatiotemporal dynamics of THM processes. Advanced time‐lapse inversion and clustering analysis quantify subsurface properties and map the heterogeneity of THM dynamics. The ERT results can estimate subsurface properties and delineate the damaged and intact zones, enabling appropriate parameterization and representation of processes for long‐term modeling. This approach may be used in further improving the predictive models and ensuring the safe long‐term disposal of radioactive waste in rock salt.

58 GEOSCIENCES↗
DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

New Geomechanical Computation Analysis Evaluating Behavior of the Fault nearby BC-4

BC-4 is an abandoned brining cavern situated in the middle of the site. Its presence poses a concern for several reasons: 1) the cavern was leached up into the caprock; 2) it is similar to BC-7, a brining cavern on the northwest corner of the dome that collapsed in 1954 and now is the home to Cavern Lake; 3) a similar collapse of BC-4 would have catastrophic consequences for the future operation of the site. There exists a previously mapped fault feature in the caprock and thought to extend into the salt dome than runs in close proximity to BC-4. There are uncertainties about the true extent of the fault, and no explicit analysis has been performed to predict the effects of the fault on BC-4 stability. Additional knowledge of the fault and its effects is becoming more crucial as an enhanced monitoring program is developed and installed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

BERT Non-Destructively Probes the Microstructure of Dissimilar Welds [Poster]

Energy-resolved neutron imaging (ERNI) utilizes contrast provided by diffraction. Microstructure with >100µm grains as in cast, annealed or welded materials is difficult to characterize by X-ray or electron-based methods $\Rightarrow$ Well-suited for neutron Bragg-edge radiography. BERT was applied to dissimilar welds of Inconel on steel produced by additive manufacturing. Viewing the sample at time-of-flight/energy/wavelength where a single grain orientation dominates the attenuation allows to map grain orientations. Similarly to electron-backscatter diffraction (EBSD) but on cm length scales and through-thickness. Combined with texture measurements on the HIPPO instrument, identifying major grain orientations, 2D and 3D grain orientation maps are feasible. Results can guide development of thermo-mechanical treatment avoiding or introducing such microstructures, characterize dissimilar welds, interpretation of acoustic signals for quality control, guide destructive examination with X-rays/electrons, or provide data for codes predicting such microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Investigation of long-lived eddies on Jupiter

Quasi-geostrophic, two layer models of the Jovian atmosphere are under development; these may be used to simulate eddy phemonena in the atmosphere and include tracer dynamics explicitly. The models permit the investigation of the dynamics of quasi-geostrophic eddies under more controlled conditions than are possible in the laboratory. They can also be used to predict the distribution and behavior of tracer species, and hence to discriminate between different models of the mechanisms forcing the eddies, provided suitable observations can be obtained. At the same time, observational strategies are being developed for the Near Infrared Mapping Spectrometer on the Galileo Orbiter, with the objective of obtaining composition measurements for comparison with the models. Maps of features at thermal infrared wavelengths near 5 micron and reflected sunlight maps as a function of wavelength and phase angle will be obtained. These should provide further useful information on the morphology, composition and microstructure of clouds within eddy features. Equilibrium chemistry models which incorporate advection may then be used to relate these results of the dynamical models and provide addtional means of classifying different types of eddies.

Lewis, S. R.↗
NASA NTRS1993

Integration of Design, Thermal, Structural, and Optical Analysis, Including Thermal Animation

In many industries there has recently been a concerted movement toward 'quality management' and the issue of how to accomplish work more efficiently. Part of this effort is focused on concurrent engineering; the idea of integrating the design and analysis processes so that they are not separate, sequential processes (often involving design rework due to analytical findings) but instead form an integrated system with smooth transfers of information. Presented herein are several specific examples of concurrent engineering methods being carried out at Langley Research Center (LaRC): integration of thermal, structural and optical analyses to predict changes in optical performance based on thermal and structural effects; integration of the CAD design process with thermal and structural analyses; and integration of analysis and presentation by animating the thermal response of a system as an active color map -- a highly effective visual indication of heat flow.

Amundsen, Ruth M.↗
NASA NTRS2023

JWST NIRCam Defocused Imaging Photometric Stability Performance and How It Can Sense Mirror Tilts

We use JWST NIRCam short-wavelength photometry to capture a transit lightcurve of the exoplanet HAT-P-14 b to assess performance as part of instrument commissioning. The short-wavelength precision is 152 ppm per 27 s integration as measured over the full time series compared to a theoretical limit of 107 ppm, after corrections to spatially correlated 1/f noise. Persistence effects from charge trapping are well fit by an exponential function with short characteristic timescales, settling on the order of 5–15 minutes. The short-wavelength defocused photometry is also uniquely well suited to measure the real-time wave-front error of JWST. Analysis of the images and reconstructed wave-front maps indicates that two different hexagonal primary mirror segments exhibited “tilt events,” where they changed orientation rapidly in less than ∼1.4 s. In some cases, the magnitude and timing of the flux jumps caused by tilt events can be accurately predicted with a telescope model. These tilt events can be sensed by simultaneous longer-wavelength NIRCam grism spectral images alone in the form of changes to the point-spread function, diagnosed from the full width at half maximum. They can also be sensed with the fine guidance sensor instrument from difference images. Tilt events possibly from sudden releases of stress in the backplane structure behind the mirrors were expected during the commissioning period because they were found in ground-based testing. Tilt events have shown signs of decreasing in frequency but have not disappeared completely. The detectors exhibit some minor (less than 1%) deviations from linear behavior in the first few groups of each integration, potentially impacting absolute fluxes and transit depths on bright targets, where only a handful of groups are possible. Overall, the noise is within 50% of the theoretical photon noise and read noise. This bodes well for high-precision measurements of transiting exoplanets and other time variable targets.

Everett Schlawin↗
DOE OSTI2021

Drell-Yan q T resummation of fiducial power corrections at N 3 LL

We consider Drell-Yan production pp → V*X → LX at small q T << Q, where q T and Q are the total transverse momentum and invariant mass of the leptonic final state L. Experimental measurements require fiducial cuts on L, which in general introduce enhanced, linear power corrections in q T /Q. We show that they can be unambiguously predicted from factorization, and resummed to the same order as the leading-power contribution. For the fiducial q T spectrum, they constitute the complete linear power corrections. We thus obtain predictions for the fiducial q T spectrum to N 3 LL and next-to-leading-power in q T /Q. Matching to full NNLO ($α$$^{2}_{s}$), we find that the linear power corrections are indeed the dominant ones, and once included by factorization, the remaining fixed-order corrections become almost negligible below q T ≲ 40 GeV. We also discuss the implications for more complicated observables, and provide predictions for the fiducial Φ* spectrum at N 3 LL+NNLO. We find excellent agreement with ATLAS and CMS measurements of q T and Φ*. We also consider the $p$$^{ℓ}_{T}$ spectrum. We show that it develops leptonic power corrections in q T /(Q – 2$p$$^{ℓ}_{T}$), which diverge near the Jacobian peak $p$$^{ℓ}_{T}$ ~ Q/2 and must be kept to all powers to obtain a meaningful result there. Doing so, we obtain for the first time an analytically resummed result for the $p$$^{ℓ}_{T}$ spectrum around the Jacobian peak at N 3 LL+NNLO. Our method is based on performing a complete tensor decomposition for hadronic and leptonic tensors. We show that in practice this is equivalent to often-used recoil prescriptions, for which our results now provide rigorous, formal justification. Our tensor decomposition yields nine Lorentz-scalar hadronic structure functions, which for Z/γ* → ℓℓ or W → ℓν directly map onto the commonly used angular coefficients, but also holds for arbitrary leptonic final states. In particular, for suitably defined Born-projected leptons it still yields a LO-like angular decomposition even when including QED final-state radiation. Finally, we also discuss the application to q T subtractions. Including the unambiguously predicted fiducial power corrections significantly improves their performance, and in particular makes them applicable near kinematic edges where they otherwise break down due to large leptonic power corrections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Identifying amyloid-related diseases by mapping mutations in low-complexity protein domains to pathologies

Proteins including FUS, hnRNPA2, and TDP-43 reversibly aggregate into amyloid-like fibrils through interactions of their low-complexity domains (LCDs). Mutations in LCDs can promote irreversible amyloid aggregation and disease. We introduce a computational approach to identify mutations in LCDs of disease-associated proteins predicted to increase propensity for amyloid aggregation. We identify several disease-related mutations in the intermediate filament protein keratin-8 (KRT8). Atomic structures of wild-type and mutant KRT8 segments confirm the transition to a pleated strand capable of amyloid formation. Biochemical analysis reveals KRT8 forms amyloid aggregates, and the identified mutations promote aggregation. Aggregated KRT8 is found in Mallory–Denk bodies, observed in hepatocytes of livers with alcoholic steatohepatitis (ASH). We demonstrate that ethanol promotes KRT8 aggregation, and KRT8 amyloids co-crystallize with alcohol. Lastly, KRT8 aggregation can be seeded by liver extract from people with ASH, consistent with the amyloid nature of KRT8 aggregates and the classification of ASH as an amyloid-related condition.

59 BASIC BIOLOGICAL SCIENCES↗