Search NASA⌕ Search

SEARCH · Search NASA

Results for “Ranking methods”

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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kincher-Winoto, Kina↗
DOE OSTI2019

Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates

Introduction: Leading cancer hospitals have increasingly shared their ‘brand’ with smaller hospitals through affiliations. Because each brand evokes a distinct reputation for the care provided, ‘brand-sharing’ has the potential to impact the public’s ability to differentiate the safety and quality within hospital networks. The general public was surveyed to determine the perceived similarities and differences in the safety and quality of complex cancer surgery performed at top cancer hospitals and their smaller affiliate hospitals. Methods: A national, web-based KnowledgePanel (GfK) survey of American adults was conducted. Respondents were asked about their beliefs regarding the quality and safety of complex cancer surgery at a large, top-ranked cancer hospital and a smaller, local hospital, both in the presence and absence of an affiliation between the hospitals. Results: A total of 1010 surveys were completed (58.1% response rate). Overall, 85% of respondents felt ‘motivated’ to travel an hour for complex surgery at a larger hospital specializing in cancer, over a smaller local hospital. However, if the smaller hospital was affiliated with a top-ranked cancer hospital, 31% of the motivated respondents changed their preference to the smaller hospital. When asked to compare leading cancer hospitals and their smaller affiliates, 47% of respondents felt that surgical safety, 66% felt guideline compliance, and 53% felt cure rates would be the same at both hospitals. Conclusions: Approximately half of surveyed Americans did not distinguish the quality and safety of surgical care at top-ranked cancer hospitals from their smaller affiliates, potentially decreasing their motivation to travel to top centers for complex surgical care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

4th Penn State Bioinorganic Workshop

The research area of biological inorganic chemistry encompasses a wide variety of subfields, including molecular biology, biochemistry, biophysics, inorganic chemistry, analytical chemistry, physical chemistry, and theoretical chemistry, as well as many different methods, such as biochemical characterization of enzymes, reaction kinetics, a plethora of spectroscopic techniques, and computational methods. The above methods are combined to understand the formation, function, and regulation of the many metallo-cofactors found in Nature as well as to identify novel metallo-cofactors. Many metalloenzyme-catalyzed reactions are extremely complex, but are of fundamental importance to science and society. Examples include (i) the reduction of the chemically inert molecule, dinitrogen, to ammonia by the enzyme nitrogenase (this reaction is fundamental for the production of nitrogen fertilizers); (ii) the oxidation of water to dioxygen by the Mn4Ca cluster found in photosystem II; and (iii) myriad reactions in which aliphatic, inert C-H bonds are cleaved for subsequent functionalization of the carbon atoms (the latter reactions are important in the biosynthesis of many natural products). Because of the broad range of areas and techniques employed in this field, research in bioinorganic chemistry is typically carried out collaboratively between two or more research groups. It is of paramount importance that researchers working in this field have a good, basic, working knowledge of many methods and approaches employed in the field, in order to design and discuss experiments with collaborators. Therefore, the training of students working in bioinorganic chemistry is an important aspect of this field. Hugely successful “bioinorganic workshops” were offered in the 1990s at The University of Georgia. These workshops laid the foundation for many of the extant collaborative research efforts in this area today. The large and diverse group of bioinorganic chemists at The Pennsylvania State University and our unique laboratory space are well suited for the continuation of such training workshops. The co-principal investigators of this award lead these efforts. After a smaller “trial workshop” in 2010, the Penn State bioinorganic group, led by the co-PIs, offers these workshops biennially. The 2012, 2014, and 2016 workshops provided training to 123, 162, and 153 participants, respectively, by offering (i) a series of lectures given by faculty experts on the given topic, (ii) hands-on training in small groups by experts in the various methods, and (iii) sharing research results of the participants by oral and poster presentations. The centerpiece of the workshops is the hands-on training, in which approximately half of the participants from all ranks (undergraduate students to faculty) served as teachers. In this section, the traditional roles of teachers and students were sometimes reversed to the extent that undergraduate students taught faculty in the students' areas of specialty. We anticipate that these workshops will facilitate research in bioinorganic chemistry and will help establish future collaborations among “workshop alumni” to carry out cutting-edge research in bioinorganic chemistry that will address many important topics relevant to our society.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Sequential design of discrete linear quadratic regulators via optimal root-locus techniques

A sequential method employing classical root-locus techniques has been developed in order to determine the quadratic weighting matrices and discrete linear quadratic regulators of multivariable control systems. At each recursive step, an intermediate unity rank state-weighting matrix that contains some invariant eigenvectors of that open-loop matrix is assigned, and an intermediate characteristic equation of the closed-loop system containing the invariant eigenvalues is created.

Shieh, Leang S.↗
NASA NTRS1991

Application of multiple criteria decision methods in space exploration initiative design and planning

Fellowship activities were directed towards the identification of opportunities for application of the Multiple Criteria Decision Making (MCDM) techniques in the Space Exploration Initiative (SEI) domain. I identified several application possibilities and proposed demonstration application in these three areas: evaluation and ranking of SEI architectures, space mission planning and selection, and space system design. Here, only the first problem is discussed. The most meaningful result of the analysis is the wide separation between the two top ranked architectures, indicating a significant preference difference between them. It must also be noted that the final ranking reflects, to some extent, the biases of the evaluators and their understanding of the architecture.

Masud, Abu S. M.↗
DOE OSTI2014

Parameterization and sensitivity analysis of a process‐based terrestrial ecosystem model using adjoint method

Abstract We developed an adjoint version of a process‐based biogeochemistry model, the Terrestrial Ecosystem Model (TEM). The adjoint model of TEM was then used to: (1) conduct sensitivity studies of net ecosystem production (NEP) for three terrestrial ecosystems: grassland, deciduous broadleaf forest and evergreen needle‐leaf forest; (2) rank the importance of parameters in controlling NEP; (3) optimize the model parameters by assimilating eddy flux data of NEP; and (4) evaluate parameterization by extrapolating optimal parameters to other sites that have the same plant functional type as the calibration sites. We found that: (1) the maximum rate of photosynthesis ( C MAX ) was the most important parameter in determining NEP, while the importance of the remaining parameters varied depending on plant functional type, suggesting that the same ecosystem process has different degrees of importance in modeling carbon fluxes; (2) the sensitivity of NEP to C MAX had a significant seasonal variability and the control of C MAX on NEP was much larger in growing season (defined as from April to October) than that in nongrowing season (defined as from December to March); and (3) after parameterization, TEM could reasonably reproduce carbon fluxes observed at eddy flux tower sites. This study provided an effective model‐data fusion framework of TEM, which could improve the future quantification of terrestrial ecosystem carbon fluxes at both site and regional levels.

Zhu, Qing↗
DOE OSTI2024

Dataset of mechanically induced thermal runaway measurement and severity level on Li-ion batteries

The deployment of Li-ion batteries covers a wide range of energy storage applications, from mobile phones, e-bikes, electric vehicles (EV) and stationary energy storage systems. However, safety issue such as thermal runaway is always one of the most important concerns to prevent Li-ion batteries from further market penetration. A standardized single-side indentation test protocol was developed to mechanically induce an internal short-circuit. The cell voltage, compressive load, indenter stroke, and temperature at the indentation point are measured in time series. The test data of each cell, along with cell parameters such as dimensions, mass, chemistry, state of charge (SOC), capacity, are integrated together to calculate a thermal runaway severity score from 0 to100. Complete data collection process including the original measured record, test method, severity score calculation scheme is presented in this article. The thermal runaway severity analysis and the more than 100 tested Li-ion battery records provide a good data source for further comparison and ranking of thermal runaway risks.

25 ENERGY STORAGE↗
DOE OSTI2014

DOC-2/DAB2 Interacting Protein Status in High-Risk Prostate Cancer Correlates With Outcome for Patients Treated With Radiation Therapy

Purpose: This pilot study investigates the role of DOC-2/DAB2 Interacting Protein (DAB2IP) and enhancer of zeste homolog 2 (EZH2) as prognostic biomarkers in high-risk prostate cancer patients receiving definitive radiation therapy. Methods and Materials: Immunohistochemistry was performed and scored by an expert genitourinary pathologist. Clinical endpoints evaluated were freedom from biochemical failure (FFBF), castration resistance–free survival (CRFS), and distant metastasis–free survival (DMFS). Log-rank test and Cox regression were used to determine significance of biomarker levels with clinical outcome. Results: Fifty-four patients with high-risk prostate cancer (stage ≥T3a, or Gleason score ≥8, or prostate-specific antigen level ≥20 ng/mL) treated with radiation therapy from 2005 to 2012 at our institution were evaluated. Nearly all patients expressed EZH2 (98%), whereas 28% of patients revealed DAB2IP reduction and 72% retained DAB2IP. Median follow-up was 34.0 months for DAB2IP-reduced patients, 29.9 months for DAB2IP-retained patients, and 32.6 months in the EZH2 study. Reduction in DAB2IP portended worse outcome compared with DAB2IP-retained patients, including FFBF (4-year: 37% vs 89%, P=.04), CRFS (4-year: 50% vs 90%, P=.02), and DMFS (4-year: 36% vs 97%, P=.05). Stratified EZH2 expression trended toward significance for worse FFBF and CRFS (P=.07). Patients with reduced DAB2IP or highest-intensity EZH2 expression exhibited worse FFBF (4-year: 32% vs 95%, P=.02), CRFS (4-year: 28% vs 100%, P<.01), and DMFS (4-year: 39% vs 100%, P=.04) compared with the control group. Conclusion: Loss of DAB2IP is a potent biomarker that portends worse outcome despite definitive radiation therapy for patients with high-risk prostate cancer. Enhancer of zeste homolog 2 is expressed in most high-risk tumors and is a less potent discriminator of outcome in this study. The DAB2IP status in combination with degree of EZH2 expression may be useful for determining patients with worse outcome within the high-risk prostate cancer population.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Global solutions of restricted open-shell Hartree-Fock theory from semidefinite programming with applications to strongly correlated quantum systems

We present a density matrix approach for computing global solutions of restricted open-shell Hartree-Fock theory, based on semidefinite programming (SDP), that gives upper and lower bounds on the Hartree-Fock energy of quantum systems. While wave function approaches to Hartree-Fock theory yield an upper bound to the Hartree-Fock energy, we derive a semidefinite relaxation of Hartree-Fock theory that yields a rigorous lower bound on the Hartree-Fock energy. We also develop an upper-bound algorithm in which Hartree-Fock theory is cast as a SDP with a nonconvex constraint on the rank of the matrix variable. Equality of the upper- and lower-bound energies guarantees that the computed solution is the globally optimal solution of Hartree-Fock theory. The work extends a previously presented method for closed-shell systems [S. Veeraraghavan and D. A. Mazziotti, Phys. Rev. A 89, 010502–R (2014)]. For strongly correlated systems the SDP approach provides an alternative to the locally optimized Hartree-Fock energies and densities with a certificate of global optimality. Applications are made to the potential energy curves of C{sub 2}, CN, Cr {sub 2}, and NO {sub 2}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints

In this work, we consider the problem of low-rank approximation of massive dense nonnegative tensor data, for example, to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes, and performing efficient and scalable parallel algorithms to compute the low-rank approximation. We present a software package called Parallel Low-rank Approximation with Nonnegativity Constraints, which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work). We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Proteome-scale Structure Prediction Data - Pseudodesulfovibrio mercurii

The number of proteins predicted for Pseudodesulfovibrio mercurii is 3,446, each of which have five predicted structures from an AlphaFold run, as well as structural alignment results using the TMscore-based structural alignment method within the APoc program. Specifically, AlphaFold outputs the atoms and coordinates of the protein model in human-readable PDB files and quantitative prediction metrics in Python PICKLE files. The 5 models have been ranked based on the predicted TM-score (pTMS), a quantitative confidence metric output by AlphaFold that reports on protein model quality. The top ranked model has undergone an energy minimization calculation to relax and remove any potential clashes in the atomic coordinates. Structural alignment results are stored in two files for each protein; the top ranked model (as discussed above) is used for all alignment analyses. Both are compressed gzip files that, once unpacked, are human readable. The first file is the TMalign score results and contains the quantitative metrics for the top alignments between the predicted structure and experimental structures from the PDB70, a curated non-redundant database of about 80,000 experimental structures developed by the Soding lab. Each data point in this file is directly associated with one experimental structure; PDB ID and brief meta-data about the protein taken from the PDB70 file are reported alongside the quantitative metrics. The second results file contains the raw results associated with each alignment reported in the score results file. Specifically, the translation and rotation arrays for each alignment are provided so that the structural alignment can be recreated. Additionally, residue-level scores are reported to quantify the closeness of the aligned residues between the predicted and experimental models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hyper-Differential Sensitivity Analysis of Uncertain Parameters in PDE-Constrained Optimization

Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional complexity, namely, uncertain parameters in the PDEs. Uncertainty quantification (UQ) is necessary to characterize, prioritize, and study the influence of these uncertain parameters. Sensitivity analysis, a classical tool in UQ, is frequently used to study the sensitivity of a model to uncertain parameters. In this article, we introduce "hyper-differential sensitivity analysis" which considers the sensitivity of the solution of a PDE-constrained optimization problem to uncertain parameters. Our approach is a goal-oriented analysis which may be viewed as a tool to complement other UQ methods in the service of decision making and robust design. We formally define hyper-differential sensitivity indices and highlight their relationship to the existing optimization and sensitivity analysis literatures. Assuming the presence of low rank structure in the parameter space, computational efficiency is achieved by leveraging a generalized singular value decomposition in conjunction with a randomized solver which converts the computational bottleneck of the algorithm into an embarrassingly parallel loop. Two multi-physics examples, consisting of nonlinear steady state control and transient linear inversion, demonstrate efficient identification of the uncertain parameters which have the greatest influence on the optimal solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1970

A bibliography of wildlife movements and tracking systems

A bibliography is presented consisting of 1,005 references concerned with animal orientation, homing, navigation, migration, and home range movements, and the various methods of tracking and monitoring such behavior through biotelemetry, radar, and various banding and tagging techniques. A majority of the publications appeared between 1950 and 1970, although the most intensive search was made of the 1965-1970 period. A small number of older articles and books were included because they appeared to have some special or unusual value. The references have been organized in two ways. First, they are grouped in primary categories on the basis of the behavior involved such as orientation, homing, etc., and the methods of tracking. Second, within each of the resulting major areas, the items are arranged in terms of the species of animal. This sequence is maintained throughout the bibliography and in many cases represents a rank order according to the number of publications found.

Werber, M.↗
NASA NTRS1977

Effect of heating rate on toxicity of pyrolysis gases from some elastomers

The effect of heating rate on the toxicity of the pyrolysis gases from six elastomers was investigated, using a screening test method. The elastomers were polyisoprene (natural rubber), styrene-butadiene rubber (SBR), ethylene propylene diene terpolymer (EPDM), acrylonitrile rubber, chlorosulfonated polyethylene rubber, and polychloroprene. The rising temperature and fixed temperature programs produced exactly the same rank order of materials based on time to death. Acrylonitrile rubber exhibited the greatest toxicity under these test conditions, and carbon monoxide was not found in sufficient concentrations to be the primary cause of death.

Hilado, C. J.↗
DOE OSTI2018

Entity Resolution at Large Scale: Benchmarking and Algorithmics

We seek scalable benchmarks for entity resolution problems. Solutions to these problems range from trivial approaches such as string sorting to sophisticated methods such as statistical relational learning. The theoretical and practical complexity of these approaches varies widely, so one of the primary purposes of a benchmark will be to quantify the trade-off between solution quality and runtime. We are motivated by the ubiquitous nature of entity resolution as a fundamental problem faced by any organization that ingests large amounts of noisy text data. A benchmark is typically a rigid specification that provides an objective measure usable for ranking implementations of an algorithm. For example the Top500 and HPCG500 bench- marks rank supercomputers based on their performance of dense and sparse linear algebra problems (respectively). These two benchmarks require participants to report FLOPS counts attainable on various machines. Our purpose is slightly different. Whereas the supercomputing benchmarks mentioned above hold algorithms constant and aim to rank machines, we are primarily interested in ranking algorithms. As mentioned above, entity resolution problems can be approached in completely different ways. We believe that users of our benchmarks must decide what sort of procedure to run before comparing implementations and architectures. Eventually, we also wish to provide a mechanism for ranking machines while holding algorithmic approach constant . Our primary contributions are parallel algorithms for computing solution quality mea- sures per entity. We find in some real datasets that many entities are quite easy to resolve while others are difficult, with a heavy skew toward the former case. Therefore, measures such as global confusion matrices, F measures, etc. do not meet our benchmarking needs. We design methods for computing solution quality at the granularity of a single entity in order to know when proposed solutions do well in difficult situations (perhaps justifying extra computational), or struggling in easy situations. We report on progress toward a viable benchmark for comparing entity resolution algo- rithms. Our work is incomplete, but we have designed and prototyped several algorithms to help evalute the solution quality of competing approaches to these problems. We envision a benchmark in which the objective measure is a ratio of solution quality to runtime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Delayed Update Algorithms for Quantum Monte Carlo Simulation on GPU: Extended Abstract

QMCPACK is open source scientific software designed to perform Quantum Monte Carlo (QMC) simulation, a first-principles method for describing many-body systems. The evaluation of each Monte Carlo move requires finding the determinant of a dense matrix in the wave functions. This calculation forms a key computational kernel in QMCPACK. After each accepted event, the wave function matrix undergoes a rank-one update to represent a single particle move within the system. The matrix inverse is updated via the Sherman-Morrison formula; occasionally, the explicit inverse must be recomputed to maintain numerical stability.; We propose an alternate approach to this kernel that utilizes QR factorization to maintain stability without refactorization. In addition, algorithms based on a novel delayed update scheme are explored in this effort. This strategy enables probability evaluation for multiple successive Monte Carlo moves, with application of accepted moves to the wave function matrix delayed until an event is denied, or a predetermined limit on acceptances p is reached. Accepted events grouped in this manner are then applied to the matrix en bloc with enhanced arithmetic intensity and computational efficiency. Delayed update Sherman-Morrison probability evaluation algorithms with CPU and GPU-accelerated implementations are tested, profiled, and analyzed. A QR based delayed update algorithm remains in development. Results are evaluated against existing methods for numerical stability and efficiency; emphasis is placed on large systems, for which acceleration is critical.

Computer Science↗
DOE OSTI2012

The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist

We play the role of an ice age ecologist (IAE) charged with conserving biodiversity during the climate changes accompanying the last deglaciation. We develop reserve-selection strategies for the IAE and check them against rankings based on modern data. Three reserve-selection strategies are developed. (1) Abiotic: the IAE uses no information about species–climate relationships, instead maximizing the climatic and geographic dispersion of reserves. (2) Species distribution models (SDMs): the IAE uses boosted-regression trees calibrated against pollen data and CCSM3 palaeoclimatic simulations from 21 to 15 ka bp to predict modern taxon distributions, then uses these as input to the Zonation reserve-ranking program. (3) Rank-and-regress: regression models are used to identify climatic predictors of zonation rankings. All strategies are assessed against a Zonation ranking based on modern pollen distributions. Analysis units are ecoregions and grid cells. The abiotic strategy has a negative or no correlation between predicted and actual rankings. The SDM-based strategy fares better, with a significantly positive area-corrected correlation (r= 0.474, P < 0.001) between predicted and actual rankings. Predictive ability drops when grid cells are the analysis unit (r= 0.217, P = 0.058). Predictive ability for the rank-and-regress strategy is similar to the SDM results. For the IAE, SDMs improve the predictive ability of reserve-selection strategies. However, predictive ability is limited overall, probably due to shifted realized niches during past no-analogue climates, new species interactions as species responded individually to climate change, and other environmental changes not included in the model. Twenty-first-century conservation planning also faces these challenges, and is further complicated by other anthropogenic impacts. The IAE's limited success does not preclude the use of climate scenarios and niche-based SDMs when developing adaptation strategies, but suggests that such tools offer at best only a rough guide to identifying possible areas of future conservation value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The impact of intensity-modulated radiation therapy plan normalization in the postprostatectomy setting—does it matter?

The International Commission on Radiation Units & Measurements -83 recommends prescribing intensity-modulated radiation therapy (IMRT) in a dose-volume manner. Despite this, clinical variation still exists in how prostate IMRT plans are prescribed. This study aims to investigate the impact of different plan normalization methods for postprostatectomy IMRT. IMRT treatment plans were created retrospectively for 20 postprostatectomy patients. These were normalized such that the dose received by 98% (D98) of the planning target volume (PTV) was equal to 100% of the prescribed dose. All plans were individually optimized to achieve target coverage and organ at risk (OAR) dose constraints. Each patient's plan was then copied and normalized such that the mean dose (Dmean) received by the PTV was equal to 100% of the prescribed dose. The International Commission on Radiation Units & Measurements -83 recommended dosimetric end points were extracted for targets and Quantitative Analyses of Normal Tissue Effects in the Clinic or Radiation Therapy Oncology Group 0534 end points extracted for OARs. Statistical analysis using the Wilcoxon signed-rank test to measure the difference between data from plans normalized to D98 and Dmean was conducted. Extracted dosimetric end points of the targets and OARs were significantly higher in plans normalized to D98 than Dmean (p Normalization impacts on dosimetric end points of a plan. Hence, reporting the normalization method used is necessary to allow for meaningful interpretation of IMRT dosimetric studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗