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At least 865 records · Page 48

Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond

Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.

big data↗

FECM/NETL CO 2 Transport Cost Model (2022): Description and User’s Manual

The FECM/NETL CO 2 Transport Cost Model (CO 2 _T_COM) is an Excel spreadsheet model that calculates the cost of transporting CO 2 from the beginning to the end of a pipeline. This document provides two main functions. First, the document describes the equations and algorithms that are used by the model to calculate technical quantities (such as the minimum inner pipe diameter needed to transport a user-specified CO 2 mass flow rate a specified distance) and engineering-economic quantities (such as capital costs, operating costs and cash flows). Second, the document is a user’s manual for the model that describes the procedures the user must follow to run the model. The document also describes input variables and output variables (i.e., results) for the model.

42 ENGINEERING↗

SimCCS 3.0 User GUIDE

Carbon Capture and Storage (CCS) is an important technology aimed at reducing CO 2 emissions and contributing to the mitigation strategies of climate change. The process involves three main steps: CO 2 capture, transport, and storage. Specifically, CO 2 is captured from industrial facilities, such as coal-fired power plants and natural gas processing plants. It is then transported through infrastructure networks, such as pipelines, rails, and ships, and injected into underground geological formations, such as depleted oil and gas reservoirs or deep saline aquifers, for permanent storage. The entire CCS value chain encompasses multiple components, including CO 2 sources, transport networks, and storage sites. The proper design of CCS transport infrastructure is an essential step for the deployment of CCS technology. However, the design is not a trivial process, as it presents many challenges and requires consideration of numerous economic, social, and environmental factors.

42 ENGINEERING↗

Chemical-Free Lithium Separation from High-Salinity Brines Using Model-Informed and Machine Learning-Optimized Multi-Column Zwitterionic Chromatography

Direct Lithium Extraction (DLE) technologies often struggle to produce high-purity lithium salts from high-salinity brines, as current approaches require chemical-based elution, regeneration, and precipitation steps, resulting in significant environmental footprints. A novel salt fractionation approach using carboxybetaine resin, known as zwitterionic chromatography (ZIC), has demonstrated that lithium ions can be separated from divalent cations under high-salinity conditions using only water as eluent, with no regeneration required. To enable continuous and scalable deployment of this approach, we developed a chemical-free Multi-column Zwitterionic Chromatography (MZC) process and its theoretical and process models. To predict and optimize this nontraditional separation system, we introduced a novel anti-Langmuir isotherm, and the isotherm parameters were estimated through a machine learning-driven optimization based on artificial neural network ensembles with numerical feasibility assessment. Using machine learning-driven optimization, the MZC process achieved 98.0% lithium recovery, 99.5 % Li/(Li + Mg + Ca) purity, a 31.3% productivity increase, and a 33% reduction in water use compared to batch operation. The proposed MZC process enables lithium separation at $0.6-1.2 kg-1 Li, with costs dominated by resin manufacturing, while offering lower separation costs and carbon footprint compared with conventional carbonation. Overall, these findings position the MZC process as an effective polishing step within scalable and sustainable lithium production pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

End-to-End Pipeline for Trigger Detection on Hit and Track Graphs

There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. Here, the triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.

97 MATHEMATICS AND COMPUTING↗

Identifying Adversarial Cyber-Activity in Operational Technology Environments Using Bayesian Networks

Critical infrastructure and other operational technology (OT) environments face increasing cybersecurity risks from adversarial behavior. This paper describes the development of a risk model using a Bayesian network to enhance the comprehension of observable cyber events caused by malicious activity in OT environments. The core of the Bayesian network is a process model that describes the stages of adversary behavior. The remainder of the model is based on the MITRE ATT&CK® for Industrial Control Systems (ICS) taxonomy, which includes tactics and techniques that may be used by the adversary. The observables provide evidence for adversary behavior through the intermediary technique and tactic nodes. One challenge in constructing this model is a lack of open-source data from cyber-attacks on OT systems. This paper discusses learning from limited data, the elicitation of expert opinion to construct the conditional probability tables when data is scarce, and the refinement of the most difficult conditional probabilities tables using several forms of sensitivity analyses. Finally, the Bayesian network is demonstrated using two historical case studies: the DarkSide ransomware attack on the Colonial Pipeline and the destructive cyberattack targeting the ThyssenKrupp blast furnace. Index Terms—Cybersecurity, industrial control systems, operational technology

97 - MATHEMATICS AND COMPUTING↗

ISO Key Project: Exploring the Full Range of Quasar/Agn Properties

While most of the work on this program has been completed, as previously reported, the portion of the program dealing with the subtopic of ISO LWS data analysis and reduction for the LWS Extragalactic Science Team and its leader, Dr. Howard Smith, is still active. This program in fact continues to generate results, and newly available computer modeling has extended the value of the datasets. As a result the team requests a one-year no-cost extension to this program, through 31 December 2004. The essence of the proposal is to perform ISO spectroscopic studies, including data analysis and modeling, of star-formation regions using an ensemble of archival space-based data from the Infrared Space Observatory's Long Wavelength Spectrometer and Short Wavelength Spectrometer, but including as well some other spectroscopic databases. Four kinds of regions are considered in the studies: (1) disks around more evolved objects; (2) young, low or high mass pre-main sequence stars in star-formation regions; (3) star formation in external, bright IR galaxies; and (4) the galactic center. One prime focus of the program is the OH lines in the far infrared. The program has the following goals: 1) Refine the data analysis of ISO observations to obtain deeper and better SNR results on selected sources. The ISO data itself underwent 'pipeline 10' reductions in early 2001, and additional 'hands-on data reduction packages' were supplied by the ISO teams in 2001. The Fabry-Perot database is particularly sensitive to noise and slight calibration errors; 2) Model the atomic and molecular line shapes, in particular the OH lines, using revised Monte-Carlo techniques developed by the SWAS team at the Center for Astrophysics; 3) Attend scientific meetings and workshops; 4) Perform E&PO activities related to infrared astrophysics and/or spectroscopy.

Wilkes, Belinda↗

Status of the TESS Science Processing Operations Center

The Transiting Exoplanet Survey Satellite (TESS) science pipeline is being developed by the Science Processing Operations Center (SPOC) at NASA Ames Research Center based on the highly successful Kepler Mission science pipeline. Like the Kepler pipeline, the TESS science pipeline will provide calibrated pixels, simple and systematic error-corrected aperture photometry, and centroid locations for all 200,000+ target stars, observed over the 2-year mission, along with associated uncertainties. The pixel and light curve products are modeled on the Kepler archive products and will be archived to the Mikulski Archive for Space Telescopes (MAST). In addition to the nominal science data, the 30-minute Full Frame Images (FFIs) simultaneously collected by TESS will also be calibrated by the SPOC and archived at MAST. The TESS pipeline will search through all light curves for evidence of transits that occur when a planet crosses the disk of its host star. The Data Validation pipeline will generate a suite of diagnostic metrics for each transit-like signature discovered, and extract planetary parameters by fitting a limb-darkened transit model to each potential planetary signature. The results of the transit search will be modeled on the Kepler transit search products (tabulated numerical results, time series products, and pdf reports) all of which will be archived to MAST.

high performance computing↗

Grid-Integrated Production of Fischer-Tropsch Synfuels from Nuclear Power

Idaho National Laboratory (INL) investigates the relative economic profitability of an integrated energy system (IES) coupling an NPP with a synfuel production process at selected case study locations across the United States. In the synfuel IES, a high-temperature steam electrolysis (HTSE) plant is thermally and electrically coupled with an NPP to produce zero-carbon hydrogen. The synthetic fuel is produced from this H 2 combined with a CO 2 supply using the reverse water gas shift process followed by the Fischer-Tropsch (FT) reaction. This analysis considers a system in which the CO 2 is sourced from regional CO 2 emitters via the construction and operation of pipeline-based CO 2 supply networks. Locating the FT plant at the same site as the NPP and HTSE plants enables the NPP to provide zero-carbon heat and power to the HTSE plant and zero-carbon power to the FT plant as well as avoid the requirement for long-distance H 2 product transport from the HTSE plant to the FT plant. Hydrogen storage is used to enable the NPP to dispatch power to the electrical grid (instead of the HTSE plant) when grid demand increases, thus enabling the FT plant to continue to operate in a steady-state production mode. The ability to cease hydrogen production for several hours within each day enables the NPP to provide power to the grid to balance the electricity market during peak periods and maximize revenues for the nuclear synfuel IES. The FT process design considered has a 99% carbon conversion efficiency. The use of nuclear energy and nuclear energy-derived hydrogen enables synfuel production to achieve this high level of carbon utilization. Additionally, the life-cycle carbon emissions of the nuclear-based synfuel production process are very low, with WTW emissions of approximately 25 gCO 2 e/MJ, including steam credits (generated from FT process excess heat), and approximately 7 gCO 2 e/MJ, if steam credits are excluded. This compares favorably with the WTW emissions of 90.5 gCO 2 e/MJ for a compression-ignition, direct injection (CIDI) vehicle with a fuel economy of 31.6 miles per gallon gasoline equivalent (MPGGE), using low-sulfur diesel produced using conventional petroleum production and refining processes. Several NPPs in various regions of the U.S. are considered as case study analyses. Supply locations and transportation via pipeline of the CO 2 feedstock to the NPP site are analyzed through the National Energy Technology Laboratory (NETL) CO 2 Transport Cost model. The team finds that the amount of CO 2 generated by different sectors is sufficient for the synfuel production process at all locations considered. The CO 2 transportation costs are functions of the distance of the source to the NPP location, the CO 2 capture cost at the source, and the quantity of CO 2 transported. Historical electricity prices for the NPP case study locations are collected and analyzed. Monthly average prices, price range, and duration of negative-price periods vary among these locations. For each location, an auto-regressive moving average (ARMA) model is trained on historical electricity price data. ARMA validation is done to ensure the synthetic price distributions represent one of historical prices with high fidelity. Synthetic time series from these ARMA models are used in a coupled dispatch and system optimization in the Holistic Energy Resource Optimization Network (HERON) to compute the differential net present value (NPV) of the IES. The team finds that this econometric is positive, ranging from $14M–1.3bn (2020) depending on the location. The optimal synfuel IES configuration to obtain this increase in NPV often maximizes the size of the synfuel production process with regards to the size of the NPP. However, the team shows that the NPP still plays a stabilizing role for the grid: In periods of high prices and high loads, more electricity from the NPP is sent to the grid. A high variability of electricity prices and extreme maximum prices tend to drive up electricity production. While it requires significant investment, the synfuel IES could increase the economic profitability for the existing fleet of LWRs across the country while still maintaining the grid stabilizer role of NPPs. During its lifetime, the main costs for the nuclear synfuel IES are the carbon feedstock transportation costs, followed by the capital expenses (CAPEX) and operation and maintenance (O&M) costs while the revenue comes first from the IRA H 2 production tax credit (PTC) and then from the sales of synfuel products. The profitability of the synfuel IES is most sensitive to the value of the hydrogen PTC and the synfuel products as well as the cost of the carbon feedstock, highlighting the importance of governmental incentives regarding hydrogen, carbon emissions, and synfuel in driving the deployment of future nuclear synfuel IESs.

08 HYDROGEN↗

ISO Key Project: Exploring The Full Range of Quasar/AGN Properties

While most of the work on this program has been completed, as previously reported, the portion of the program dealing with the sub topic of ISO LWS data analysis and reduction for the LWS Extragalactic Science Team and its leader, Dr. Howard Smith, is still active. This program in fact continues to generate results, and newly available computer modeling has extended the value of the datasets, As a result the team has requested and been granted an obtained a no-cost extension to this program, through December 31, 2003. The essence of the proposal is to perform ISO spectroscopic studies, including data analysis and modeling, of star formation regions using an ensemble of archival space-based data from the Infrared Space Observatory's Long Wavelength Spectrometer and Short Wavelength Spectrometer, but including as well some other spectroscopic data bases. Four kinds of regions are considered in the studies: (1) disks around more evolved objects; (2) young, low or high mass pre-main sequence stars in star formation regions; (3) star formation in external, bright IR galaxies; and (4) the galactic center. One prime focus of the program is the OH lines in the far infrared. The program has the following goals: (1) refine the data analysis of ISO observations, to obtain deeper and better SNR results on selected sources. The ISO data itself underwent "pipeline 10" reductions in early 2001, and additional "hands-on data reduction packages" were supplied by the ISO teams in 2001. The Fabry-Perot database in particularly sensitive to noise can slight calibration errors. (2) model the atomic and molecular line shapes, in particular the OH lines, using revised Monte-Carlo techniques developed by the SWAS team at the Center for Astrophysics; (3) attend scientific meetings and workshops; (4) do E&PO activities related to infrared astrophysics and/or spectroscopy.

Wilkes, Belinda↗

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗

In situ tumor model for longitudinal in silico imaging trials

Abstract Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast’s soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50μm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion’s local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Engineering↗

Alerga: Alert Aggregation and Reasoning in GOOSE Simulation Pipeline

IEC 61850 specifies the Generic Object Oriented Substation Event (GOOSE) protocol as one option for low latency communication of substation-related events. Due to its strict timing requirements, GOOSE lacks any form of encryption or authentication and has only minimal integrity guarantees. These absences render the protocol vulnerable to a variety of communication anomalies, including adversarial action. In particular, an adversary with access to the substation network can launch man in the middle (MITM) attacks. We propose Alerga, a set of tools to allow operators to mitigate some of the risks of the protocol while retaining its strengths. To that end, we have developed first a GOOSE simulation pipeline including data generation, anomaly detection, alert handling, causal reasoning and data visualization components. The simulator is designed to be modular, allowing operators to swap components to better fit their network capabilities. The volume of alert traffic on a substation network threatens operators with alert fatigue. In order to combat this, we secondly present a novel form of alert aggregation and processing, offering operators a condensed view of any threats to the system. Thirdly, to facilitate the handling of these threats, our causal reasoning system traces the alerts back to their most likely cause, generating an initial hypothesis for operators to investigate.

alert aggregation↗

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

36 MATERIALS SCIENCE↗

Introducing NOODLES: Collaborative Visualization in a Flavorful Package

We introduce NOODLES, a lightweight collaborative visualization and analysis protocol. NOODLES was developed to enable new scientific workflows while enhancing existing pipelines. Using a technology-minimal approach to strengthen applicability, this protocol allows researchers to tie disparate software together as well as making domain specific visualizations accessible across various platforms and form factors. In this talk we discuss background, challenges, and gaps in the current tool landscape that lead to the development of the protocol. We then describe the design of the protocol and present several use cases to demonstrate the efficacy of NOODLES in addressing complex visualization challenges and how it co-exists with existing tools. We conclude by outlining in-progress work and potential avenues for future development within the NOODLES framework.

collaborative analysis↗

Laue-DIALS: Open-source software for polychromatic x-ray diffraction data

Most x-ray sources are inherently polychromatic. Polychromatic (“pink”) x-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure—ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most x-ray facilities to discard >99% of x-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

97 MATHEMATICS AND COMPUTING↗

GL4U: Using Space Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. In June 2022, GL4U partnered with Jet Propulsion Laboratory’s (JPL) Planetary Protection Center of Excellence to conduct the indirect training pilot program by training educators at historically black colleges and universities (HBCUs) and minority serving institutions (MSIs). During the educator pilot, participants received materials, training, and will be provided the necessary compute resources to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U training program provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. Pre- and post-bootcamp surveys were completed by all participants and show the overwhelming success of the bootcamps.

Amanda M. Saravia-Butler↗

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗