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Jim Bishop, senior scientist at Berkeley Lab and professor at UC Berkeley, is leading a project to deploy robotic floats that provide data on how microorganisms sequester carbon in the ocean. He recently led a research team on a 10-day voyage, funded by the National Science Foundation, to put the Carbon Flux Explorers to the test.
Jim Bishop, senior scientist at Berkeley Lab and professor at UC Berkeley, is leading a project to deploy robotic floats that provide data on how microorganisms sequester carbon in the ocean. He recently led a research team on a 10-day voyage, funded by the National Science Foundation, to put the Carbon Flux Explorers to the test. Read more at http://oceanbots.lbl.gov (Video edited by Roxanne Makasdjian and Stephen McNally/UC Berkeley. Video shot by Jessica Kendall-Bar. Drone footage provided by Mark Booher.)
The U.S. Department of Energy, Office of Nuclear Energy (DOE/NE) has sponsored a program since Fiscal Year (FY) 09 to develop the technical basis for extended dry storage of used fuel. This program is also working to develop the transportation technical basis for the transport of used fuel after the extended storage period. As this program has progressed, data gaps associated with dry storage systems (e.g., fuel, cask internals, canister, closure, overpack, and pad) have been identified that need to be addressed to develop the technical bases for extended storage and transportation. There has also been an initiation of experimental testing and analyses based on the identified data gaps. The technical aspects of the NE program are being conducted by a multi-lab team made up of the DOE laboratories. As part of this program, a mission objective is to also collaborate closely with industry and the international sector to ensure that all the technical issues are addressed and those programs outside the DOE program can be leveraged, where possible, to maximize the global effort in storage and transportation research. The DOE/NE program is actively pursuing the development of the technical basis to demonstrate the feasibility of storing UNF for extended periods of time with subsequent transportation of the UNF to its final disposition. This program is fully integrated with industry, the U.S. regulator, and the international community to assure that programmatic goals and objectives are consistent with a broad perspective of technical and regulatory opinion. As the work evolves, assessments will be made to ensure that the work continues to focus on the overall goals and objectives of the program. (authors)
Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).
Submersible multistage centrifugal pumps are ideal for pumping in narrow confined spaces and achieving necessary head pressures and flow rates. Once a diameter is determined then manipulation of the number of stages and motor speed are all that are required to meet desired flow conditions. The Versatile Test Reactor (VTR) closed loop cartridge systems will need forced convection cooling independent of the main reactor. A multistage centrifugal pump can meet the necessary flow rates and pumping pressures while minimizing space taken. The pump considered for this work was based off a deep well submersible pump, a variation of a multistage centrifugal pump. We experimented with two pump sizes, 5 cm (2 inch) and 7.5 cm (3 inch) diameters. These diameters were chosen to fit into the inner diameter of standard 5 and 7.5 (2 and 3 inch) Schedule 40 pipe, respectively. This made the design for the test loop both simpler and less expensive as the need for an engineered pump housing was eliminated. Initial test cartridge planning indicated space for only a 5 cm (2 inch) diameter pump, though early testing of this size showed the need for an abnormally high-speed and high-power motor. Fine tuning of the cartridge design allowed a pump size increase to 7.5 cm (3 inches), which was the pump size most extensively tested in this work. The test loop is composed of various sizes of PVC and aluminum piping components in a loop configuration. The pump is driven by a Pittman 250 W (1/3 horsepower) electric motor with maximum speed of 3,450 RPM. Testing consisted of running the pump at a constant motor speed while varying a control valve to restrict flow through the loop, with differential pressure and flow rate recorded. This was done for one and two stage configurations for the 5 cm (2 inch) diameter impeller design and one, two, and three stage configurations for the 7.5 cm (3 inch diameter) impeller design, respectively. Due to pumping power requirements, two and three stage 7.5 cm (3 inch) diameter impeller testing at higher flowrates lowered the motor speed substantially. In regions where motor speed could not be maintained constant, the data were discarded. The test loop was also reconfigured to allow for the pump to be tested for pressure drop in a stalled or inoperable (0 RPM) flow condition. Demonstration of adequate natural convection cooling of the test cartridge fuel type is necessary under accident conditions, and this will depend upon the flow resistance through the impeller assembly when the pump is not operating. Thus, accurate knowledge of the effective impeller assembly loss coefficient is important for safety evaluations. The test loop was modified to provide water inlet and outlets on either side of the pump impeller stack, and a metered flow of lab water was provided in order to measure the pressure drop across the cartridges as a function of flowrate. Data from the pump head curve testing developed as part of this work and supported by analysis using pump head affinity laws indicates that a three stage 7.5 cm (3 inch) pump impeller design will meet target requirements for coolant flow within the VTR cartridge sodium cartridge at full power conditions [1] with margin; this corresponds to a flowrate of 45 l/min (12 gpm) at a pressure drop of 6.1 m (20 feet) of water head. The results of the pressure loss measurements across the impeller assembly when the pump is stationary (i.e., at 0 RPM) indicate that the pressure loss coefficient is 0.921 for a two impeller stack configuration; this value is calculated based on the flow velocity through the minimum available flow area within a single stage of the impeller which corresponds to 1.4 cm2.
Multimedia learning has always been a topic in physics education—whether through specialized digital data acquisition systems, simulations, or animations. Due to increasing digitalization, more and more media from our everyday lives have also found their way into education in recent decades. This contribution introduces a conceptual framework and illustrative examples for AI-supported experimentation in physics education. It does not yet present empirical data but outlines directions for forthcoming classroom studies evaluating learning outcomes and engagement.
Abstract Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high‐quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K ‐means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In‐Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.
The spin structure of the nucleon can play a key testing ground for Quantum Chromo-Dynamics (QCD) at wide kinematic ranges from smaller to large four momentum transfer Q{sup 2}. The pioneering experiments have confirmed several QCD sum rules at high Q{sup 2} where a perturbative picture holds. For a full understanding of QCD at various scales, various measurements were made at intermediate and small Q{sup 2} region and their interpretation would be a challenging task due to the non-perturbative nature. Jefferson Lab has been one of the major experimental facilities for the spin structure with its polarized electron beams and various polarized targets. A few QCD sum rules have been compared with the measured spin structure functions g{sub 1}(x, Q{sup 2}) and g{sub 2}(x, Q{sup 2}) at low Q{sup 2} and surprising results have been obtained for the spin polarizabilities, {gamma}{sub 0} and {delta}{sub LT} . As for the proton spin structure functions, the lack of data for g{sub 2}(x,Q{sup 2}) structure functions has been complemented with a new experiment at Jefferson Lab, SANE. The results from SANE will provide a better picture of the proton spin structure at a wide kinematic range in x and Q{sup 2}.
The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.
The DuraMAT Data Hub has evolved to meet the ever expanding research demands by integrating a chatbot interface that enhances FAIR compliance and simplifies discovery across our growing archive of projects and datasets. With the Data Hubs increasing use by both consortium researchers and the international community, traditional navigation methods have become unwieldy. Building on last year's feasibility study, we now report the successful implementation of the chatbot system, detailing its architecture and demonstrating its ability to deliver a more intuitive and rewarding user experience.
In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.
New results are reported from a measurement of $\pi^0$ electroproduction near threshold using the p(e, e´p) π⁰ reaction. The experiment was designed to determine precisely the energy dependence of $s-$ and $p-$wave electromagnetic multipoles as a stringent test of the predictions of Chiral Perturbation Theory (ChPT). The data were taken with an electron beam energy of 1192 MeV using a two-spectrometer setup in Hall A at Jefferson Lab. For the first time, complete coverage of the $\phi^*_{\pi}$ and $\theta^*_{\pi}$ angles in the $p \pi^0$ center-of-mass was obtained for invariant energies above threshold from 0.5 MeV up to 15 MeV. The 4-momentum transfer $Q^2$ coverage ranges from 0.05 to 0.155 (GeV/c)$^2$ in fine steps. A simple phenomenological analysis of our data shows strong disagreement with $p-$wave predictions from ChPT for $Q^2>0.07$ (GeV/c)$^2$, while the $s-$wave predictions are in reasonable agreement.
Exclusive meson electroproduction at different squared four-momenta of the exchanged virtual photon, Q 2 , and at different four-momentum transfers, t and u, can be used to probe QCD's transition from hadronic degrees of freedom at the long distance scale to quark-gluon degrees of freedom at the short distance scale. Backward-angle meson electroproduction was previously ignored, but is anticipated to offer complimentary information to conventional forward-angle meson electroproduction studies on nucleon structure. This work is a pioneering study of backward-angle ω cross sections through the exclusive 1 H(e, e'p)ω reaction using the missing mass reconstruction technique. The extracted cross sections are separated into the transverse (T), longitudinal (L), and LT, TT interference terms. The analyzed data were part of experiment E01-004 (Fπ-2), which used 2.6-5.2 GeV electron beams and HMS+SOS spectrometers in Jefferson Lab Hall C. The primary objective was to detect coincidence π in the forward-angle, where the backward-angle omega events were fortuitously detected. The experiment has central Q 2 values of 1.60 and 2.45 GeV 2 , at W = 2.21 GeV. There was significant coverage in phi and epsilon, which allowed separation of σ T,L,LT,TT . The data set has a unique u coverage of -u ~ 0, which corresponds to -t > 4 GeV 2 . The separated σ T result suggest a flat ~ 1/Q 1.33±1.21 dependence, whereas sigma_L seems to hold a stronger 1/Q 9.43±6.28 dependence. The σL/σ T ratio indicate σ T dominance at Q 2 = 2.45 GeV 2 at the ~90% confidence level. After translating the results into the -t space of the published CLAS data, our data show evidence of a backward-angle omega electroproduction peak at both Q 2 settings. Previously, this phenomenon showing both forward and backward-angle peaks was only observed in the meson photoproduction data. Through comparison of our σ T data with the prediction of the Transition Distribution Amplitude (TDA) model, and signs of σ T dominance, promising indications of the applicability of the TDA factorization are demonstrated at a much lower Q 2 value than its preferred range of Q 2 > 10 GeV 2 . These studies have opened a new means to study the transition of the nucleon wavefunction through backward-angle experimental observables.
We performed an image search for 'Comet Holmes', using the Yahoo{exclamation_point} Web search engine, on 2010 April 1. Thousands of images were returned. We astrometrically calibrated-and therefore vetted-the images using the Astrometry.net system. The calibrated image pointings form a set of data points to which we can fit a test-particle orbit in the solar system, marginalizing over image dates and detecting outliers. The approach is Bayesian and the model is, in essence, a model of how comet astrophotographers point their instruments. In this work, we do not measure the position of the comet within each image, but rather use the celestial position of the whole image to infer the orbit. We find very strong probabilistic constraints on the orbit, although slightly off the Jet Propulsion Lab ephemeris, probably due to limitations of our model. Hyperparameters of the model constrain the reliability of date meta-data and where in the image astrophotographers place the comet; we find that {approx}70% of the meta-data are correct and that the comet typically appears in the central third of the image footprint. This project demonstrates that discoveries and measurements can be made using data of extreme heterogeneity and unknown provenance. As the size and diversity of astronomical data sets continues to grow, approaches like ours will become more essential. This project also demonstrates that the Web is an enormous repository of astronomical information, and that if an object has been given a name and photographed thousands of times by observers who post their images on the Web, we can (re-)discover it and infer its dynamical properties.
Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.
The Project focused on mitigation of degradation processes on membrane electrode assemblies. The approach was to develop a model to improve understanding of the mechanisms, and to use it to focus mitigation strategies. The detailed effects of various accelerated stress tests (ASTs) were evaluated to determine the best subset to use in model development. A combination of ASTs developed by the Fuel Cell Commercialization Conference of Japan and the Fuel Cell Tech Team were selected for use. The ASTs were compared by measuring effects on performance, running in-situ diagnostics, and performing microscopic analyses of the membrane electrode assemblies after the stress tests were complete. Nissan ran FCCJ AST protocols and performed in situ and ex-situ electrochemical testing. DuPont ran FCTT and USFCC AST protocols, performed scanning and transmission electron microscopy and ran in-situ electrochemical tests. Other ex-situ testing was performed by IIT, along with much of the data analysis and model development. These tests were then modified to generate time-dependent data of the degradation mechanisms. Three different catalyst types and four membrane variants were then used to generate data for a theoretically-based degradation model. An important part of the approach was to use commercially available materials in the electrodes and membranes made in scalable semiworks processes rather than lab-based materials. This constraint ensured all materials would be practicable for full-scale testing. The initial model for the electrode layer was tested for internal consistency and agreement with the data. A Java-based computer application was developed to analyze the time-dependent AST data using polarization curves with four different cathode gas feeds and generate model parameters. Data showed very good reproducibility and good consistency as cathode catalyst loadings were varied. At the point of termination of the project, a basic electrode model was in hand with several areas identified for improvement. Time dependence and the membrane portion of the model were not addressed due to cancellation of Phase 2 of the Project.
This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with spectra containing similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and qualitative distribution analysis. Additionally, we propose a probabilistic classification model that can utilize spectral data, temporal data, or both to determine if the incorporation of temporal information improves radioisotope identification. Our findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated to develop more robust and optimal methods for utilizing this temporal information in applications requiring radioisotope identification.
Phisherman is an online software tool that was created to help experimenters study phishing. It can potentially be re-purposed to run other human studies. Phisherman enables studies to be run online, so that users can participate from their own computers. This means that experimenters can get data from subjects in their natural settings. Alternatively, an experimenter can also run the app online in a lab-based setting, if that is desired. The software enables the online deployment of a study that is comprised of three main parts: (1) a consent page, (2) a survey, and (3) an identification task, with instruction/transition screens between each part, allowing the experimenter to provide the user with instructions and messages. Upon logging in, the subject is taken to the permission page, where they agree to or do not agree to take part in the study. If the subject agrees to participate, then the software randomly chooses between doing the survey first (and identification task second) or the identification task first (and survey second). This is to balance possible order effects in the data. Procedurally, in the identification task, the software shows the stimuli to the subject, and asks if she thinks it is a phish (yes/no) and how confident she is about her answer. The subject is given 5 levels of certainty to select from, labeled "low" (1), to "medium" (3), to "high" (5), with the option of picking a level between low and medium (2), and between medium and high (4). After selecting his/her confidence level, then the "Next" button activates, allowing a user to move to the next email. The software saves a given subject's progress in the identification task, so that she may log in and out of the site. The consent page is a space for the experimenter to provide the subject with human studies board /internal review board information, and to formally consent to participate in the study. The survey is a space for the experimenter to provide questions and spaces for the users to input answers (allowing both multiple-choice and free-answer options). Phisherman includes administrative pages for managing the stimuli and users. This includes a tool for the experimenter to create, preview, edit, delete (if desired), and manage stimuli (emails). The stimuli may include pictures (uploaded to an appropriate folder) and links, for realism. The software includes a safety feature that prevents the user from going to any link location or opening a file/image. Instead of re-directing the subject's browser, the software provides a pop-up box with the URL location of where the user would have gone. Another administrative page may be used to create fake subject accounts for testing the software prior to deployment, as well as to delete subject accounts when necessary. Data from the experiment can be downloaded from another administrative page.