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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.

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At least 451 records · Page 25

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).

Chemistry↗

Integrating Carbon Capture, Utilization, & Sequestration into Chemical Pulp Mills

The U.S. pulp and paper industry presents a unique and largely untapped opportunity for large- scale carbon dioxide removal (CDR). Unlike most industrial sectors, pulp mills rely heavily on biomass, meaning that much of their carbon emissions originate from atmospheric CO₂ that was recently captured by plants. If this biogenic CO₂ can be captured and permanently stored, pulp mills can be transformed from carbon emitters into net carbon removal facilities. This project was motivated by that opportunity and aimed to develop and evaluate integrated, low-cost strategies for capturing, utilizing, and sequestering CO₂ within existing chemical pulping operations. The scope of this work focused on four complementary innovations designed to integrate seamlessly into kraft pulp mill infrastructure: (1) in situ CO₂ capture within the recovery cycle, (2) oxy-fuel retrofitting of the rotary lime kiln to produce a high-purity CO₂ stream, (3) ex situ CO₂ capture and mineralization using pulp mill residues (dregs, grits, and lime mud), and (4) beneficial reuse of these residues as mineral carbonate fertilizers. The project combined process modeling, laboratory experimentation, life cycle assessment (LCA), and field trials to evaluate the technical feasibility, economic viability, and environmental impact of these approaches. The results demonstrate that pulp mills can serve as effective platforms for carbon removal when equipped with integrated carbon capture systems. Process modeling showed that combining sodium spiking with oxy-fuel calcination significantly enhances CO₂ capture efficiency while reducing costs by up to 31% compared to conventional configurations. Experimental work further revealed that calcination behavior in high-CO₂ environments differs substantially from traditional systems, leading to the development of a new kinetic model that predicts reaction rates under these conditions. This model provides essential design guidance for next-generation decarbonized lime kilns. In parallel, the project demonstrated that alkaline mineral residues generated during pulping operations can be repurposed as a sustainable alternative to agricultural lime. Across a wide range of soils in the southeastern United States, these materials performed equivalently to commercial lime in adjusting soil pH while offering lower greenhouse gas emissions and reduced cost. Field and greenhouse studies confirmed that crop and tree growth responses were comparable, supporting their viability as a drop-in replacement. This co-product pathway provides a practical utilization strategy that offsets costs and improves overall system economics. A major contribution of this project is the first comprehensive life cycle assessment of carbon removal in pulp and paper systems across multiple system boundaries. Results show that retrofitted mills can achieve carbon removal efficiencies ranging from 12% to 92%, depending on how the system is defined. This finding highlights a critical issue in carbon accounting: reported performance is highly sensitive to methodological choices. By explicitly quantifying these differences, this work provides valuable guidance for policymakers, carbon registries, and project developers working to standardize carbon removal metrics. From a commercialization perspective, the technologies investigated in this project are well- aligned with existing industrial infrastructure, minimizing the need for entirely new facilities. 3 DE-EE0009413 Industry engagement throughout the project—including collaboration with pulp and paper companies, equipment manufacturers, and carbon removal developers—has accelerated the transition from research to deployment. Notably, a commercial developer is actively pursuing carbon capture projects at pulp mills in the southeastern United States and has cited this research as a contributing foundation. The emergence of voluntary carbon markets and long-term offtake agreements further strengthens the business case for implementation. The broader public benefits of this work are significant. By enabling large-scale carbon removal using existing industrial systems, this approach offers a near-term pathway to reduce atmospheric CO₂ concentrations while supporting domestic manufacturing and rural economies. The reuse of industrial residues as fertilizers reduces reliance on mined materials, lowers costs for farmers, and decreases environmental impacts associated with conventional lime production. In addition, the project has supported workforce development by training graduate students and researchers in carbon capture technologies, helping to build capacity in a critical area of national interest. In conclusion, this project demonstrates that integrated carbon capture, utilization, and sequestration in pulp mills is both technically feasible and economically promising. By combining process innovation, experimental validation, and systems-level analysis, the work advances the understanding of how biomass-based industries can contribute to climate mitigation. The findings provide a strong foundation for commercial deployment and offer a scalable solution for transforming a major U.S. industry into a source of durable carbon removal.

09 BIOMASS FUELS↗

Integrated optical frequency division for microwave and mmWave generation

Abstract The generation of ultra-low-noise microwave and mmWave in miniaturized, chip-based platforms can transform communication, radar and sensing systems 1–3 . Optical frequency division that leverages optical references and optical frequency combs has emerged as a powerful technique to generate microwaves with superior spectral purity than any other approaches 4–7 . Here we demonstrate a miniaturized optical frequency division system that can potentially transfer the approach to a complementary metal-oxide-semiconductor-compatible integrated photonic platform. Phase stability is provided by a large mode volume, planar-waveguide-based optical reference coil cavity 8,9 and is divided down from optical to mmWave frequency by using soliton microcombs generated in a waveguide-coupled microresonator 10–12 . Besides achieving record-low phase noise for integrated photonic mmWave oscillators, these devices can be heterogeneously integrated with semiconductor lasers, amplifiers and photodiodes, holding the potential of large-volume, low-cost manufacturing for fundamental and mass-market applications 13 .

Science & Technology - Other Topics↗

Thermo-mechanical deterioration and molecular degradation of 3D-printed methacrylate-based polymer in various chemical environments

The advancement of additive manufacturing (AM) has accelerated the development of stereolithographic (SLA) photo-curable resins, particularly methacrylate-based polymers, due to the ability to their high-resolution, robust mechanical properties, and suitability. However, their long-term performance in chemical environments remains poorly understood. This study investigates the extent and mechanisms of degradation on an SLA-printed methacrylate-based polymer subjected to various chemicals, including polar and non-polar solvents, as well as strongly acidic aqueous solutions over a 12-week accelerated aging period. A comprehensive analytical approach incorporating swelling kinetics, surface morphology, tensile and dynamic mechanical analysis (DMA), Fourier-transform infrared (FTIR) spectroscopy, and mass spectrometry (MS) was employed to characterize chemical absorption, structural integrity, and leached products. Results reveal that degradation severity is governed by both the polarity and reactivity of the chemical environment. Notably, exposure to 6 mol L -1 HNO₃ induced the most severe deterioration, with over threefold higher swelling compared to other media, and significant reductions in tensile strength, tensile modulus, and glass transition temperature (T g ). In contrast, specimens aged in non-polar solvents (xylene and dodecane) exhibited negligible chemical interaction and retained mechanical performance. FTIR and MS analyses identified acid-catalyzed hydrolysis of ester groups as prominent degradation pathways in acidic media, while diffusion-controlled plasticization prevailed in polar solvents. Furthermore, this study provides valuable insights into the chemical stability of SLA-printed polymers and develops predictive degradation profiles that are crucial for designing durable polymer systems for advanced industrial use.

36 MATERIALS SCIENCE↗

Ion-Specific Precipitation of Extractants Enables Rare-Earth Separation and Wastewater Remediation from Solvent Extraction of Critical Elements

The increasing demand for rare-earth elements (REEs) necessitates sustainable recovery strategies, particularly from secondary sources, such as electronic waste. Solvent extraction is the primary industrial method for REE separation; however, the unintentional dissolution of extractants into wastewater poses serious environmental risks, leading to organic contamination and process inefficiencies. Existing wastewater treatment methods struggle to remove these persistent pollutants, underscoring the need for innovative recovery approaches. Herein, we present a ligand-mediated precipitation strategy that simultaneously recovers REEs and removes dissolved extractants from solvent extraction wastewater. We show that residual extractants in the aqueous phase can selectively bind REEs, inducing their precipitation while leaving transition metals in solution. By integrating FTIR spectroscopy, EDS, XPS, EXAFS, and SAXS, we elucidate the mechanism of ion-specific precipitation and the local coordination environment of metal ions in the precipitate. Importantly, we demonstrate that the precipitated extractants can be efficiently recovered and reused, providing a closed-loop solution that enhances sustainability. Applying this method to leachates from samarium–cobalt (Sm–Co) and neodymium–iron–boron (NdFeB) mixed magnets, we achieve highly selective REE precipitation under mild conditions, demonstrating a scalable and cost-effective pathway for REE recovery, wastewater purification, and extractant recycling. In conclusion, by integrating element-specific ligand-mediated precipitation with extractant reuse, this work offers a transformative approach to REE separation that reduces the environmental impact while improving resource efficiency.

E-waste↗

Evolution of silicate coordination in architected amorphous and crystalline magnesium silicates during carbon mineralization

Advancing durable solutions for carbon storage and removal at the gigaton scale to produce solid carbonates via carbon mineralization requires harnessing earth abundant magnesium silicate resources. Calibrated insights linking the structural and morphological features of earth abundant amorphous and crystalline magnesium silicate phases to their reactivity are essential for scalable deployment but remain underdeveloped. To resolve the influence of silica coordination and mass transfer on carbon mineralization behavior, magnesium silicates bearing amorphous and crystalline phases (AC Mg-silicate) are synthesized. The structural and morphological transitions starting from colloidal precursors to their final synthesized form on heating are delineated using operando ultra small/small/wide angle X-ray scattering (USAXS/SAXS/WAXS) measurements. The evolution of the silicate phases on carbon mineralization of AC Mg-silicate is contrasted with that of highly crystalline Mg-silicate (HC Mg-silicate) when reacted at 200 °C and a CO 2 partial pressure of 20 atm in water and 1 M NaHCO 3 solution in stirred and unstirred environments. These experimental conditions are analogous to those of the water–gas-shift reaction for sustainable recovery of H 2 with inherent carbon mineralization. Enhancement in the extent of carbon mineralization by 13.3–19.5% noted in the presence of NaHCO 3 compared to water in AC and HC Mg-silicate with and without stirring, is attributed to the buffering effect which aids simultaneous silicate dissolution and carbon mineralization. Enhanced extents of carbon mineralization in the presence of NaHCO 3 correspond to the formation of MgSiO 3 and SiO 2 phases from the starting Mg 2 SiO 4 precursors in AC and HC Mg-silicate. Unlocking these silicate transformations during carbon mineralization by harnessing architected Mg-silicate precursors reveals the feasibility of integrating these chemical pathways with sustainable H 2 conversion pathways with inherent carbon mineralization.

Gao, Xun [Cornell Univ., Ithaca, NY (United States↗

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

79 ASTRONOMY AND ASTROPHYSICS↗

Physics-constrained deep learning of nonlinear normal modes of spatiotemporal fluid flow dynamics

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.

Mechanics↗

Design and performance of AI agents interfacing with an atomic layer deposition tool

In this work, we introduce the design of an atomic layer deposition (ALD) reactor augmented with an AI interface for autonomous materials synthesis. Our modular design encapsulates the particularities of the hardware behind a Python interface that communicates with the ALD control software via transmission control protocol. This interface is compatible with model context protocol interfaces used in agentic frameworks. We have integrated our tool with a simple AI agent that leverages a large language model to transform user-supplied queries into ALD processes that are then run in our reactor. Our approach uses a JavaScript object notation schema to encode ALD processes. Our experimental results show that the AI interface does not impose a significant overhead to our control software, at least within our fastest 10 ms scale. We also carried out a detailed evaluation of the agent performance using leading models in two classes of tasks: basic instruction and process discovery tasks, where the agent is presented with a target material and needs to identify the correct ALD process compatible with the reactor configuration. Despite the simplicity of our agent design, we observed that most of the advanced models excelled at the instruction tasks. However, only recent models, such as o1, o3, GPT-5, and Claude Opus 4, performed well in process discovery tasks. We also observed significant variability in the response for the hardest challenges. While the results obtained are promising, we identify areas where AI research could improve the performance of agents for ALD.

47 OTHER INSTRUMENTATION↗

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗

New limit on dark photon kinetic mixing in the 0.2 – 1.2 μ eV mass range from the Dark E-field Radio experiment

We report new limits on the kinetic mixing strength of the dark photon spanning the mass range 0.21 − 1.24 μ eV corresponding to a frequency span of 50–300 MHz. The Dark E-field Radio experiment is a wideband search for dark photon dark matter. In this paper we detail changes in calibration and upgrades since our proof-of-concept pilot run. Our detector employs a wide-bandwidth E-field antenna moved to multiple positions in a shielded room, a low noise amplifier, wideband analog-to-digital converter, followed by a 2 24 -point fast Fourier transform. An optimal filter searches for signals with Q ≈ 10 6 . In nine days of integration, this system is capable of detecting dark photon signals corresponding to a kinetic mixing strength ε several orders of magnitude lower than previous limits. We find a 95% exclusion limit on ε over this mass range between 6 × 10 − 15 and 6 × 10 − 13 , tracking the complex resonant mode structure in the shielded room. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗

HydraGNN 2.0

HydraGNN is an Oak Ridge National Laboratory (ORNL)-branded implementation of distributed multi-tasking graph neural networks that supports several scientific applications within the ORNL portfolio to support the US-DOE mission. New or improved capabilities included in v2.0.0 release are as follows: 1) Enhancement in message passing layers through class inheritance 2) Adding transformation to ensure translation and rotation invariance 3) Supporting various optimizers 4) Atomic descriptors 5) Integration with continuous CI test 6) Distributed printouts and timers 7) Profiling 8) Support of ADIOS2 for scalable data loading 9) Large-scale system support, including Summit (ORNL) and Perlmutter (NERSC)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

hkl-projects/ioc-hkl

HKL-IOC is an open source EPICS IOC that performs real‑time crystallographic HKL calculations for diffractometers and scattering instruments. It integrates the Python hkl library with EPICS through PyDevice, exposing HKL calculations and diffractometer geometry transformations as standard EPICS process variables. This allows beamline and laboratory control systems to convert between motor positions and reciprocal‑space coordinates, configure diffractometer geometries, and drive scans directly in HKL space. The software is written in Python and designed to run alongside existing EPICS deployments without requiring changes to core EPICS components. It is intended for use at synchrotron and neutron scattering facilities, as well as laboratory X‑ray diffractometers, where reliable and reproducible HKL calculations are needed for experiment control, data collection, and automation. HKL-IOC is distributed under the GNU General Public License v3.0 (GPL‑3.0) and contributions and extensions for additional geometries and beamlines are welcomed.

Baekey, Alex↗

Novel additive manufacturing for plasma facing materials ‐ creating a research pathway for minority students

This project addresses two critical and intertwined challenges in fusion energy, namely the shortage of a broadly trained scientific workforce and the lack of scalable manufacturing solutions for plasma-facing components (PFCs). Through a collaboration among Florida International University (FIU), Miami Dade College (MDC), and Purdue University, the project established structured, reproducible educational and research pathways that recruit and advance students from institutions historically outside the fusion energy enterprise, building the human capital that this field urgently needs. The project integrates the complementary research strengths of FIU and Purdue to investigate flash sintering as a transformative processing route for tungsten-based PFCs. Unlike conventional sintering approaches, flash sintering offers rapid densification at significantly reduced thermal budgets, making it a compelling candidate for fabricating complex tungsten geometries that must withstand extreme plasma-facing environments. Systematic experimental and modeling efforts will elucidate the fundamental mechanisms governing microstructure evolution, grain boundary chemistry, and thermomechanical response during flash sintering — knowledge that is presently lacking but essential for translating this technology into reliable manufacturing practice. The convergence of workforce development and cutting-edge manufacturing research positions this project to deliver measurable, durable impact: a pipeline of fusion-ready researchers cultivated through expanded institutional partnerships, and a validated materials processing framework that accelerates domestic readiness for next-generation fusion reactor construction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Morouço, Pedro (ORCID:0000000259569790)↗

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗