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At least 253 records · Page 14

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Multivariate Bayesian analysis can bring significant benefits to seismic hazard analysis: Its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure; and Its updating feature enables a seamless integration of new ground motion data into the hazard results. Here, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault-type, magnitude, and distance dependencies, and also the linear and the rock intensity dependent site response. We use a hybrid Markov Chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis-Hastings step. We then perform several checks on the model and note that its performance is satisfactory. Finally, we illustrate the merits of this multivariate Bayesian analysis, which include: ground motion model updating with ground motion data recorded in the last four years not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

58 GEOSCIENCES↗

Neutronics Phenomena Important in Modeling and Simulation of Liquid-Fuel Molten Salt Reactors

This paper discusses liquid-fuel molten salt reactors, how they will operate under normal, transient, and accident conditions, and the results of an expert elicitation to determine the corresponding neutronic phenomena important to understanding their behavior. Identifying these phenomena will enable the U.S. Nuclear Regulatory Commission (NRC) to develop or identify modeling functionalities and tools required to carry out confirmatory analyses that examine the validity and accuracy of applicants’ calculations and help determine the margin of safety in plant design. NRC frequently does an expert elicitation using a Phenomena Identification and Ranking Table (PIRT) to identify and evaluate the state of knowledge of important modeling phenomena. However, few details about the design of these reactors and the sequence of events during accidents are known, so the process used was considered a preliminary PIRT. A panel met to define phenomena that would need to be modeled and considered the impact/importance of each phenomenon with respect to specific figures-of-merit (FoMs) (e.g., power distribution, fluence, kinetics parameters and reactivity). Each FoM reflected a potential impact on radionuclide release or loss of a barrier to release. The panel considered what the path forward might be with respect to being able to model the phenomenon in a simulation code. Results are explained for both thermal and fast spectrum designs.

42 ENGINEERING↗

Computational and Experimental Study for the Denitrification of Biomass-Derived Hydrothermal Liquefaction Oil

Hydrothermal liquefaction (HTL) is a promising method for processing wet biomass and waste feedstock to produce biofuels. During the HTL process, proteins and other biomolecules in certain feedstock get converted into nitrogenous compounds in produced biocrude, which represents a major challenge to further upgrading them into fuels. One promising approach is to separate nitrogenous compounds from the biocrude using polymeric resins. In this study, experiments were conducted to down-select sorbent and resin systems using a nitrogen-compound-containing surrogate biocrude. We model the binding interactions between an Amberlyst polymeric resin with various compounds present in the biocrude mixture such as nitrogenous compounds like pyrrole, pyridine, hexanamide, and representative co-existing compounds like phenol and dodecanoic acid. To ascertain the efficiency of various resins in the denitrogenation process, we have developed a quantitative structure–function model for the interacting components in the mixture. Our results suggest that the Amberlyst resin is a viable candidate for efficient removal of target nitrogen-containing compounds (such as pyridine) from the biocrude as a result of favorable interactions. The computational studies provide some insight into how and why the identified resin (Amberlyst) works in selective extraction of nitrogenous compound(s).

09 BIOMASS FUELS↗

Maximum entropy models of ecosystem functioning

Using organism-level traits to deduce community-level relationships is a fundamental problem in theoretical ecology. This problem parallels the physical one of using particle properties to deduce macroscopic thermodynamic laws, which was successfully achieved with the development of statistical physics. Drawing on this parallel, theoretical ecologists from Lotka onwards have attempted to construct statistical mechanistic theories of ecosystem functioning. Jaynes’ broader interpretation of statistical mechanics, which hinges on the entropy maximisation algorithm (MaxEnt), is of central importance here because the classical foundations of statistical physics do not have clear ecological analogues (e.g. phase space, dynamical invariants). However, models based on the information theoretic interpretation of MaxEnt are difficult to interpret ecologically. Here I give a broad discussion of statistical mechanical models of ecosystem functioning and the application of MaxEnt in these models. Emphasising the sample frequency interpretation of MaxEnt, I show that MaxEnt can be used to construct models of ecosystem functioning which are statistical mechanical in the traditional sense using a savanna plant ecology model as an example.

54 ENVIRONMENTAL SCIENCES↗

Thermal-Hydraulics Phenomena Important in Modeling and Simulation of Liquid-Fuel Molten Salt Reactors

This paper discusses liquid-fuel molten salt reactors, how they will operate under normal, transient, and accident conditions, and the results of an expert elicitation to determine the corresponding thermalhydraulic phenomena important to understanding their behavior. Identifying these phenomena will enable the U.S. Nuclear Regulatory Commission (NRC) to develop or identify modeling functionalities and tools required to carry out confirmatory analyses that examine the validity and accuracy of an applicant’s calculations and help determine the margin of safety in plant design. NRC frequently does an expert elicitation using a Phenomena Identification and Ranking Table (PIRT) to identify and evaluate the state of knowledge of important modeling phenomena. However, few details about the design of these reactors and the sequence of events during accidents are known, so the process used was considered a preliminary PIRT. A panel met to define phenomena that would need to be modeled and considered the impact/importance of each phenomenon with respect to specific figures-of-merit (FoMs) (e.g., salt temperature, velocity, and composition). Each FoM reflected a potential impact on radionuclide release or loss of a barrier to release. The panel considered what the path forward might be with respect to being able to model the phenomenon in a simulation code. Results are explained for both thermal and fast spectrum designs.

42 ENGINEERING↗

Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals.

09 BIOMASS FUELS↗

Thermal-Hydraulic and Neutronic Phenomena Important in Modeling and Simulation of Liquid-Fuel Molten Salt Reactors

In this paper, we discuss liquid-fuel molten salt (cooled) reactors (MSRs); how they will operate under normal, transient, and accident conditions; and the results of an expert elicitation to determine the corresponding thermal-hydraulic and neutronic phenomena important to understanding their behavior. Identifying these phenomena will enable the U.S. Nuclear Regulatory Commission (NRC), U.S. Department of Energy, and industry to develop or identify modeling functionalities and tools required to carry out confirmatory and licensing analyses that examine the validity and accuracy of an applicant's calculations and help determine the margin of safety in plant design. The NRC frequently does an expert elicitation using a Phenomena Identification and Ranking Table (PIRT) to identify and evaluate the state of knowledge of important modeling phenomena. However, few details about the design of these reactors and the sequence of events during accidents are known, so the process used was considered a preliminary PIRT. A panel comprising a group of subject matter experts met to define phenomena that would need to be modeled and considered the impact/importance of each phenomenon with respect to specific figures of merit (FoMs) (e.g., salt temperature, velocity, and composition). Each FoM reflected a potential impact on radionuclide release or loss of a barrier to release. The panel considered what the path forward might be with respect to being able to model the phenomenon in a simulation code. Results are explained for both thermal and fast spectrum designs, with an emphasis on the thermal-hydraulic takeaways. It was concluded that compared to light water reactors, the lack of high-pressure operation, energetic break flow, depressurization, and quench front tracking may simplify some aspects of an MSR analysis. However, MSRs have new phenomena both for a license applicant and NRC confirmatory analysis. There is a need for enhanced understanding of physical properties for MSRs that encompass several individual thermophysical properties, including thermal conductivity, viscosity, specific heat, density, optical properties, thermodynamic properties, volatilities, solubilities, etc. Salt composition is closely linked to both these properties and the neutronics of the system. Additionally, the large number of MSR concepts and system designs means that there is wide variation in the potential modeling needs for these systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Off-shell pion properties: Electromagnetic form factors and light-front wave functions

The off-shell pion electromagnetic form factors are explored with corresponding off-shell light-front wave functions modeled by constituent quark and antiquark. We apply the Mandelstam approach for the microscopic computation of the form factors relating the model parameters with the pion decay constant and charge radius. Analyzing the existing data on the cross sections for the Sullivan process, 1 H(e,e',π + )⁢n, Charged pion form factor between Q 2 = 0.60, and 2.45 GeV 2 . I. Measurements of the cross section for the 1 H⁡(e,e'⁢π + )⁢n reaction, we extract the off-shell pion form factor using the relation derived from the generalized Ward-Takahashi identity for the pion electromagnetic current. They are compared with our previous results from exactly solvable manifestly covariant model of a (3+1)-dimensional fermion field theory. We find that the adopted constituent quark model reproduces the extracted off-shell form factor F 1 ⁡(Q 2 ,t) from the experimental data within a few percent difference and matches well with our previous theoretical simulation which exhibits a variation of about 10% for the extracted off-shell pion form factor g(Q 2 ,t). We also identify the pion valence parton distribution function (PDF) and transverse momentum distribution (TMD) in terms of the light-front wave function and discuss their off-shell properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Cloud Atlas: High-contrast Time-resolved Observations of Planetary-mass Companions

Directly imaged planetary-mass companions offer unique opportunities in atmospheric studies of exoplanets. They share characteristics of both brown dwarfs and transiting exoplanets, and therefore are critical for connecting atmospheric characterizations for these objects. Rotational phase mapping is a powerful technique to constrain the condensate cloud properties in ultra-cool atmospheres. Applying this technique to directly imaged planetary-mass companions will be extremely valuable for constraining cloud models in low mass and surface-gravity atmospheres and for determining the rotation rate and angular momentum of substellar companions. Here, we present Hubble Space Telescope Wide Field Camera 3 near-infrared time-resolved photometry for three planetary-mass companions, AB Pic B, 2M0122B, and 2M1207b. Using two-roll differential imaging and hybrid point-spread function modeling, we achieve sub-percent photometric precision for all three observations. We find tentative modulations (<2σ) for AB Pic B and 2M0122B, but cannot reach conclusive results on 2M1207b due to strong systematics. The relatively low significance of the modulation measurements cannot rule out the hypothesis that these planetary-mass companions have the same vertical cloud structures as brown dwarfs. Our rotation rate measurements, combined with archival period measurements of planetary-mass companions and brown dwarfs, do not support a universal mass-rotation relation. The high precision of our observations and the high occurrence rates of variable low-surface-gravity objects encourage high-contrast time-resolved observations with the James Webb Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

Mechanism-Based Design of Green Oxidation Catalysts

In modern era of scarce resources, developing chemical processes that can eventually generate useful materials and fuels from readily available, simple, cheap, renewable starting materials is of paramount importance. Small molecules, such as dioxygen, dinitrogen, water, or carbon dioxide, can be viewed as ideal sources of oxygen, nitrogen, or carbon atoms in synthetic applications. Living organisms perfected the art of utilizing small molecules in biosynthesis and in generating energy; photosynthesis, which couples carbohydrate synthesis from carbon dioxide with photocatalytic water splitting, is but one impressive example of possible catalytic processes. Small molecule activation in synthetic systems remains challenging, and current efforts are focused on developing catalytic reactions that can convert small molecules into useful building blocks for generating more complicated organic molecules, including fuels. Modeling nature is attractive in many respects, including the possibility to use non-toxic, earth-abundant metals in catalysis. Specific systems investigated in our work include biomimetic catalytic oxidations with dioxygen, hydrogen peroxide, and related oxygen atom donors. More recently, a new direction was been also pursued in the group, fixation of carbon dioxide with transition metal complexes. Mechanistic understanding of biomimetic metal-catalyzed oxidations is critical for the design of functional models of metalloenzymes, and ultimately for the rational synthesis of useful, selective and efficient oxidation catalysts utilizing dioxygen and hydrogen peroxide as terminal oxidants. All iron oxidases and oxygenases (both mononuclear and dinuclear) utilize metal-centered intermediates as reactive species in selective substrate oxidation. In contrast, free radical pathways (Fenton chemistry) are common for traditional inorganic iron compounds, producing hydroxyl radicals as very active, non-selective oxidants. Recent developments, however, changed this situation. Growing families of synthetic iron complexes that resemble active sites of metalloenzymes produce metal-based intermediates (rather than hydroxyl radicals) in reactions with oxygen donors. These complexes are very promising for selective oxygen and peroxide activation. In order to understand the mechanisms of metal-based small molecule activation, kinetically competent metal-oxygen intermediates must be identified. One of the grand challenges identified by the Department of Energy workshop "Catalysis for Energy" is understanding mechanisms and dynamics of catalyzed reactions. The research summarized herein focuses on detailed characterization of the formation and reactivity of various iron-peroxo- and iron-oxo intermediates that are involved in catalysis. Rates of rapid reactions were studied at low temperatures by a specialized technique termed cryogenic stopped-flow spectrophotometry. These measurements identified reaction conditions which favor the formation of catalytically competent oxidants. Chemical structures of reactive complexes was determined, and new, efficient catalysts for hydrocarbon oxidation were synthesized. Importantly, these catalysts are selective, they promote oxidation of hydrocarbons at a specific site. The catalysts are also efficient and robust, hundreds of cycles of substrate oxidation occur within minutes at room temperature. Furthermore, they enable utilization of environmentally friendly oxidants, such as hydrogen peroxide, which produces water as the only byproduct. Mechanistic insights uncovered the role of various acid-containing additives in catalytic oxidations. Proton delivery to the active catalytic sites facilitated oxidations, similarly to the catalytic pathways in metal-containing enzymes. Under certain conditions, two metals in one complex can act in concert, modeling the reactivity of a bacterial enzyme which converts methane into methanol. In related studies, a family of nickel complexes that react with carbon dioxide at the rates comparable to enzyme carbonic anhydrase, was discovered. Sequestration and chemical utilization of carbon dioxide is one of the important goals in energy production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production.

09 BIOMASS FUELS↗

Neutrino–oxygen CC0 π scattering in the SuSAv2-MEC model

We present the predictions of the SuSAv2-MEC model for the double differential charged-current muonic neutrino (antineutrino) cross section on water for the T2K neutrino (antineutrino) beam. We validate our model by comparing with the available inclusive electron scattering data on oxygen and compare our predictions with the recent T2K $\nu_\mu$-$^{16}$O data, finding good agreement at all kinematics. We show that the results are very similar to those obtained for $\nu_\mu-^{12}$C scattering, except at low energies, and we comment on the origin of this difference. A factorized spectral function model of $^{16}$O is also included for purposes of comparison.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Modeling of dielectric function in plasmonic quantum dot nanolaser

In this work we present a model of the dielectric function in plasmonic quantum dot (QD) nanolaser. A metal/semiconductor/metal structure was considered to attain plasmonic nanocavity with active region containing: QD, wetting layer and barrier. The dielectric function was calculated for both metal (Ag) and QD structure. The propagation constant of surface plasmon polariton (SPP) at the interface of Ag/InAs-QD structure was calculated and the dispersion relation of the plasmonic QD structure was evaluated. For frequencies far from plasma one, the gap between real and imaginary parts was large and a deviation from linear relation was obvious. The SPP field was strongly localized at the interface due to the effect of zero-dimensional QD structure which has application in the super-resolution and best sensitivity in optical imaging. Results of propagation length of SPP (L{sub spp}) also support this. According to the L{sub spp} results, the damping in the SPP energy was low in the Ag/InAs-QD compared to that in the Ag/air interface. The obtained results are in the range of experimental ones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗

Impact of Wide-Area Oscillation Damping Control using Measurement-Driven Approach on System Separation - Saudi Grid Case Study

In any interconnected power grid, low-frequency oscillations is a major problem that can limit the power transfer capability and deteriorate power system security due to potential low-damped or even undamped oscillations. Synchronized measurements provided by PMUs enable the design and development of wide-area oscillation damping controllers (WADC) based on measurement-driven model to overcome the limitations of traditional controllers. This work focuses on the impact of a W ADC based on a measurement-driven approach on system separation for the Saudi Electricity Company (SEC) grid. A grid model is provided by SEC for this study. Modal analysis is performed to identify the oscillation mode of interest for which the controller is designed. Damping the dominant oscillation mode helps slightly to improve transient stability of the system. Considering bus frequency at different locations in SEC's system as the candidate observation signals, the optimal observation signal is chosen for the W ADC by using the FFT method. The system transfer function model is constructed by utilizing probing measurements. Then, the W ADC parameters are calculated based on the identified model. The performance of the designed W ADC is tested under different contingencies.

Altarjami, Ibrahim↗

Model-based real-time surface heat flux and temperature estimation for the DIII-D tokamak

A control-oriented model for monitoring of wall power flux densities on the DIII-D tokamak has been successfully implemented and validated experimentally. Future reactors will have to withstand severe steady state high heat flux loads on plasma-facing components (PFCs). Due to the difficulty of directly-measuring local heat fluxes on these components, monitoring and protection of PFCs during the plasma discharge can benefit from simplified physics-based real-time (RT) functional models to estimate and guide heat load control. As a first step into the development, a control-oriented model for monitoring of wall power flux densities and temperatures on DIII-D tokamak has been successfully implemented. The paper discusses the experimental demonstration and comparison of the 2-D model-based wall heat flux algorithm on the DIII-Dinner wall limiter (IWL) against infra-red camera heat flux measurements for limited plasma configurations. The paper also reports on the benchmarking of the field line tracing environment, SMITER, developed at ITER organization on DIII-D tokamak against experimental IR diagnostic data and the derivation of the component shaping weighting factors for the 2-D model-based approach. Here, the extension of the model-based approach for surface temperature estimation on the DIII-D IWL is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗