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The proceedings of the conference were published online and in book form. The online version can be found at: https://www.epj-conferences.org/articles/epjconf/abs/2024/06/contents/contents.html.
This document describes the development of a field programable gate array (FPGA) radio frequency system on a chip (RF SoC) adaptation and evaluation of the single-board device to perform both Frequency Domain Reflectometry (FDR) and Spread Spectrum Time Domain Reflectometry (SSTDR) for offline and online cable testing. The work builds on and leverages the work of Pacific Northwest National Laboratory (PNNL) in airport millimeter wave technology by using the same development hardware employed in that program. The work is performed under sponsorship from the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program and the task objective is to confirm and demonstrate feasibility to adapt FPGA technology for a cost-effective multiplexed single-board electronic module to perform cable tests that are equivalent to commercial and laboratory test instruments for FDR and SSTDR cable tests. The developed 2-channel (extendable to 7 channels) system was compared to dedicated and proven test instruments and shown to produce equivalent results on a range of cables and with a range of damage types. The FPGA reflectometry test board is one of several technologies that could facilitate implementation of online monitoring of safety critical cable systems.
The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.
The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.
The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinolessconversion of a muon into an electron in the field of an aluminum nucleus with a sensitivityimprovement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data AcquisitionSystem (TDAQ) usesotsdaqas the online Data Acquisition System (DAQ) solution. Developed atFermilab,otsdaqintegrates both theartdaqDAQ and theartanalysis frameworks for event transfer,filtering, and processing.otsdaqis an online DAQ software suite with a focus on flexibility andscalability and provides a multi-user, web-based, interface accessible through a web browser. Thedata stream from the detector subsystems is read by a software filter algorithm that selects eventswhich are combined with the data flux coming from a Cosmic Ray Veto System. The DetectorControl System (DCS) has been developed using the Experimental Physics and Industrial ControlSystem (EPICS) open source platform for monitoring, controlling, alarming, and archiving. TheDCS System has been integrated intootsdaq. A prototype of the TDAQ and the DCS systems hasbeen built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress ofthe integration of this prototype in the onlineotsdaqsoftware.
The Hadron Calorimeter (HCAL) in the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) was recently upgraded for Run 3 to introduce depth segmentation and online timing measurements, expanding the physics capabilities. In particular, the augmentation of the calorimeter information at the hardware trigger level enables quick identification and recording of long-lived particle decays using lower thresholds on energy-based event quantities. The depth segmentation and online timing capabilities are utilized in novel HCAL-based hardware-level triggers to identify displaced and delayed long-lived particles (LLPs), either decaying inside the calorimeter volume or arriving at a delayed time. This increases the sensitivity to LLP decays occurring up to almost 6 m from the collision point. This two-pronged calorimeter trigger approach leverages the new capabilities of the CMS HCAL to expand the phase space accessible in ongoing LLP searches. These triggers were deployed for Run 3 of the LHC, beginning data-taking in 2022, and we review the trigger implementation and calibration.
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.
Objectives Among the various impacts of disasters in terms of emotions, quarantine has been proven to result in significant increases in mental health problems. Studies of psychological resilience during outbreaks of epidemics tend to focus on long-term social quarantine. In contrast, insufficient studies have been conducted examining how rapidly negative mental health outcomes occur and how these outcomes change over time. We evaluated the time course of psychological resilience (over three different phases of quarantine) among students at Shanghai Jiao Tong University to investigate the influence of unexpected changes on college students. Methods An online survey was conducted from 5 to 7 April 2022. A structured online questionnaire was administered using a retrospective cohort trial design. Before 9 March (Period 1), individuals engaged in their usual activities without restrictions. From 9 to 23 March (Period 2), the majority of students were asked to remain in their dormitories on campus. From 24 March to early April (Period 3), restrictions were relaxed, and students were gradually allowed to participate in essential activities on campus. We quantified dynamic changes in the severity of students’ depressive symptoms over the course of these three periods. The survey consisted of five sets of self-reported questions: demographic information, lifestyle/activity restrictions, a brief mental health history, COVID-19-related background, and the Beck Depression Inventory, second edition. Results A total of 274 college students aged 18–42 years (mean = 22.34; SE = 0.24) participated in the study (58.39% undergraduate students, 41.61% graduate students; 40.51% male, 59.49% female). The proportion of students with depressive symptoms was 9.1% in Period 1, 36.1% in Period 2, and 34.67% in Period 3. Depressive symptoms increased notably with the introduction of the quarantine in Periods 2 and 3. Lower satisfaction with the food supplied and a longer duration of physical exercise per day were found to be positively associated with changes in depression severity in Periods 2 and 3. Quarantine-related psychological distress was more evident in students who were in a romantic relationship than in students who were single. Conclusion Depressive symptoms in university students rapidly increased after 2 weeks of quarantine and no perceptible reversal was observed over time. Concerning students in a relationship, ways to take physical exercise and to relax should be provided and the food supplied should be improved when young people are quarantined.
Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.
A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.
T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) to histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis by MS can detect species missed by bottom-up approaches. We used a novel approach of online two-dimensional liquid chromatography-tandem MS with high-resolution reversed-phase liquid chromatography (RPLC), alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. The first online RPLC separation sorted histone families, then RPLC or weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative identifications were assigned by matching proteoform masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for histone-intact protein PTM mapping (HiPTMap) to identify and quantify proteoforms purified from CD8 T cells after in vivo influenza infection. Activation significantly altered PTMs following influenza infection, histone maps changed as T cells migrated to the site of infection, and T cells responding to secondary infections had significantly more transcription enhancing modifications. Thus, HiPTMap identified and quantified proteoforms and determined changes in CD8 T cell histone PTMs over the course of infection.
We present a systematic search for optical counterparts to 13 gravitational wave (GW) triggers involving at least one neutron star during LIGO/Virgo's third observing run (O3). We searched binary neutron star (BNS) and neutron star black hole (NSBH) merger localizations with the Zwicky Transient Facility (ZTF) and undertook follow-up with the Global Relay of Observatories Watching Transients Happen (GROWTH) collaboration. The GW triggers had a median localization area of 4480 deg 2 , a median distance of 267 Mpc, and false-alarm rates ranging from 1.5 to 10 -25 yr -1 . The ZTF coverage in the g and r bands had a median enclosed probability of 39%, median depth of 20.8 mag, and median time lag between merger and the start of observations of 1.5 hr. The O3 follow-up by the GROWTH team comprised 340 UltraViolet/Optical/InfraRed (UVOIR) photometric points, 64 OIR spectra, and three radio images using 17 different telescopes. We find no promising kilonovae (radioactivity-powered counterparts), and we show how to convert the upper limits to constrain the underlying kilonova luminosity function. Initially, we assume that all GW triggers are bona fide astrophysical events regardless of false-alarm rate and that kilonovae accompanying BNS and NSBH mergers are drawn from a common population; later, we relax these assumptions. Assuming that all kilonovae are at least as luminous as the discovery magnitude of GW170817 (-16.1 mag), we calculate that our joint probability of detecting zero kilonovae is only 4.2%. If we assume that all kilonovae are brighter than-16.6 mag (the extrapolated peak magnitude of GW170817) and fade at a rate of 1 mag day -1 (similar to GW170817), the joint probability of zero detections is 7%. If we separate the NSBH and BNS populations based on the online classifications, the joint probability of zero detections, assuming all kilonovae are brighter than -16.6 mag, is 9.7% for NSBH and 7.9% for BNS mergers. Moreover, no more than <57% (<89%) of putative kilonovae could be brighter than -16.6 mag assuming flat evolution (fading by 1 mag day -1 ) at the 90% confidence level. If we further take into account the online terrestrial probability for each GW trigger, we find that no more than <68% of putative kilonovae could be brighter than -16.6 mag. Comparing to model grids, we find that some kilonovae must have M ej < 0.03 M ⊙ , X lan > 10 -4 , or φ > 30° to be consistent with our limits. We look forward to searches in the fourth GW observing run; even 17 neutron star mergers with only 50% coverage to a depth of-16 mag would constrain the maximum fraction of bright kilonovae to <25%.
Abstract Understanding the details of r -process nucleosynthesis in binary neutron star merger (BNSM) ejecta is key to interpreting kilonova observations and identifying the role of BNSMs in the origin of heavy elements. We present a self-consistent, two-dimensional, ray-by-ray radiation-hydrodynamic evolution of BNSM ejecta with an online nuclear network (NN) up to a timescale of days. For the first time, an initial numerical relativity ejecta profile composed of the dynamical component and spiral-wave and disk winds is evolved including detailed r -process reactions and nuclear heating effects. A simple model for the jet energy deposition is also included. Our simulation highlights that the common approach of relating in postprocessing the final nucleosynthesis yields to the initial thermodynamic profile of the ejecta can lead to inaccurate predictions. Moreover, we find that neglecting the details of the radiation-hydrodynamic evolution of the ejecta in nuclear calculations can introduce deviations of up to 1 order of magnitude in the final abundances of several elements, including very light and second r -process peak elements. The presence of a jet affects element production only in the innermost part of the polar ejecta, and it does not alter the global nucleosynthesis results. Overall, our analysis shows that employing an online NN improves the reliability of nucleosynthesis and kilonova light-curve predictions.
The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the initial design value, at $\sqrt{s}$ = 13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.
This data set comprises cumulative ambient ice-nucleating particle (INP) concentrations measured at the National Oceanic and Atmospheric Administration's (NOAA’s) Barrow Atmospheric Baseline Observatory (71.3230° N, 156.6114° W, “BRW” hereafter), next to the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site and ~ 6 km northeast of the town of Utqiaġvik. Our INP abundance data were generated using a combination of online instrument, the Portable Ice Nucleation Experiment chamber ver. 3 (PINE-03), and an offline cold stage, the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT). Our online INP data are all from the Examining the Ice-Nucleating Particles from NSA (ExINP-NSA) campaign conducted from October 19, 2021 to May 24, 2024. The offline INP concentration analysis was performed at West Texas A&M University for aerosol particle samples collected on polycarbonate filters (with 0.2-micron diameter pores). The PINE-03 measurements, as well as sampling activities for offline INP measurements, were conducted using the BRW site. An inset laminar sampling stack was mounted to the instrument platform, allowing PINE-03 to intake particle-laden air. For most of the campaign period, the semi-autonomous PINE-03 chamber was remotely controlled from West Texas A&M University using the LabView interface through the BeyondTrust remote-access console. PINE-03 was set to conduct an immersion freezing expansion experiment (i.e., simulated adiabatic cooling along with RHw at or above 100%). Except during the scheduled maintenance periods, the time resolution of each expansion experiment was approximately 12 minutes. PINE-03 continuously measured INP concentrations during the entire campaign without any substantial breaks. For most of the campaign period, PINE scanned its set-point vessel air temperatures from -14 °C to -31 °C and back to -14 °C about every 120 minutes.
This data set comprises cumulative ambient ice-nucleating particle (INP) concentrations measured at the National Oceanic and Atmospheric Administration's (NOAA’s) Barrow Atmospheric Baseline Observatory (71.3230° N, 156.6114° W, “BRW” hereafter), next to the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site and ~ 6 km northeast of the town of Utqiaġvik. Our INP abundance data were generated using a combination of online instrument, the Portable Ice Nucleation Experiment chamber ver. 3 (PINE-03), and an offline cold stage, the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT). Our online INP data are all from the Examining the Ice-Nucleating Particles from NSA (ExINP-NSA) campaign conducted from October 19, 2021 to May 24, 2024. The offline INP concentration analysis was performed at West Texas A&M University for aerosol particle samples collected on polycarbonate filters (with 0.2-micron diameter pores). The PINE-03 measurements, as well as sampling activities for offline INP measurements, were conducted using the BRW site. An inset laminar sampling stack was mounted to the instrument platform, allowing PINE-03 to intake particle-laden air. For most of the campaign period, the semi-autonomous PINE-03 chamber was remotely controlled from West Texas A&M University using the LabView interface through the BeyondTrust remote-access console. PINE-03 was set to conduct an immersion freezing expansion experiment (i.e., simulated adiabatic cooling along with RHw at or above 100%). Except during the scheduled maintenance periods, the time resolution of each expansion experiment was approximately 12 minutes. PINE-03 continuously measured INP concentrations during the entire campaign without any substantial breaks. For most of the campaign period, PINE scanned its set-point vessel air temperatures from -14 °C to -31 °C and back to -14 °C about every 120 minutes.
Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.