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403 records · Page 23

A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases

In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

McClay, Wilbert↗

The grism lens-amplified survey from space (glass). VII. The diversity of the distribution of star formation in cluster and field galaxies at 0.3 ≤ z ≤ 0.7

Exploiting the slitless spectroscopy taken as part of the Grism Lens-Amplified Survey from Space (GLASS), we present an extended analysis of the spatial distribution of star formation in 76 galaxies in 10 clusters at 0.3<z<0.7. We use 85 foreground and background galaxies in the same redshift range as a field sample. The samples are well matched in stellar mass (10{sup 8}−10{sup 11} M{sub ⊙}) and star formation rate (0.5–50 M{sub ⊙} yr{sup −1}). We visually classify galaxies in terms of broad band morphology, Hα morphology, and likely physical process acting on the galaxy. Most Hα emitters have a spiral morphology (41% ± 8% in clusters, 51% ± 8% in the field), followed by mergers/interactions (28% ± 8%, 31% ± 7%, respectively) and early-type galaxies (remarkably as high as 29% ± 8% in clusters and 15% ± 6% in the field). A diversity of Hα morphologies is detected, suggesting a diversity of physical processes. In clusters, 30% ± 8% of the galaxies present a regular morphology, mostly consistent with star formation diffused uniformly across the stellar population (mostly in the disk component, when present). The second most common morphology (28% ± 8%) is asymmetric/jellyfish, consistent with ram-pressure stripping or other non-gravitational processes in 18% ± 8% of the cases. Ram-pressure stripping appears significantly less prominent in the field (2% ± 2%), where the most common morphology/mechanism appears to be consistent with minor gas-rich mergers or clump accretion. This work demonstrates that while environment-specific mechanisms affect galaxy evolution at this redshift, they are diverse and their effects are subtle. A full understanding of this complexity requires larger samples and detailed and spatially resolved physical models.

79 ASTRONOMY AND ASTROPHYSICS↗

Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314,000 galaxies

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.

79 ASTRONOMY AND ASTROPHYSICS↗

Utility-Scale Solar, 2024 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2024 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Key findings from this year’s report include: -18.5 GWAC of new utility-scale PV capacity came online in 2023, bringing cumulative installed capacity to more than 80.2 GWAC across 47 states. Installed costs continued to fall in 2023. Relative to 2022, capacity-weighted averages decreased by 8% to -$\$1.43$/WAC (or $\$1.08$/WDC). Costs, based on a 7.1 GWAC sample of 76 plants completed in 2023, have fallen by 75% (averaging 10% annually) since 2010. Plant-level capacity factors vary widely, from 6% to 36% (on an AC basis), with a sample median of 24%. -Levelized cost of energy (LCOE) of new 2023 projects increased slightly to $\$46$/MWh prior to the application of tax credits but continued to fall to $\$31$/MWh when accounting for federal incentives. PPA prices have largely followed the decline in solar’s LCOE over time, but newly signed longer-term PPA prices have increased since 2021, to an average of $\$35$/MWh (levelized, in 2023 dollars). -Solar’s average energy and capacity value (i.e., ability to offset costs of other power generation sources) across the U.S. was $\$45$/MWh in 2023. Solar’s average market value was lowest in CAISO ($\$27$/MWh), the market with the greatest solar generation share, and highest in ERCOT ($\$67$/MWh). -Newer solar projects had greater market value in 2023 than their generation costs, yielding $\$1.1$ billion in benefits. Projects built in 2022 delivered on average $\$15$/MWh more market value than their costs in 2023. -Solar’s combined value from wholesale electricity markets, public health and climate damage reduction were greater than generation costs and incentives, yielding $\$13.7$ billion in net benefits in 2023. We estimate U.S. health benefits of $\$24$/MWh and reduced global climate damages of $\$101$/MWh. -Adding battery storage is one way to increase the value of solar. Deployment of 52 new PV+battery hybrid plants set a record with 5.3 GW installed in 2023. Our public data file tracks metadata and PPA prices from more than 100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -Looking ahead, a massive pipeline of at least 1,085 GW of solar capacity dominates the nation’s interconnection queues at the end of 2023. Nearly 571 GW, or 53%, of that total was paired with a battery – in CAISO it was a staggering 98%. Historically only 10% of the requested solar capacity is built. -For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗

Comparison of Mutual Awareness in Analog vs. Digital Control Rooms

Control rooms in nuclear power plants are complex, collaborative working environments rife with potential for human error. As control rooms evolve from analog to digital interface environments, crew communication strategies must change as well. Operators in the analog control room operate the plant and communicate with each other with the help of control boards with large control panels, meters, and switches. Reactor operators could choose a particular set of controls on the particular control board which corresponds to the part of the system of interest. Visual display units (VDU), which became a fixture in the control rooms with the shift to digital human-machine interfaces (HMI), bring benefits for safety and flexibility combining information from various sources. With the increase in automation and the use of digital HMIs operators received their personal sitting workstations where they are able to navigate and operate the plant. The amount of information increased as well as the complexity of monitoring and analyzing the system states. Also computerized systems cause operators to get absorbed by their personal screens and reduce interaction between the crew members. Therefore, the collaboration and coordination of task demands requires vigilance and mutual awareness. The level of automation has an influence on the level of mutual awareness experienced by operators in the control rooms. Teams function to carry out highly interdependent tasks, requiring synthesis and sharing of information to achieve common goals. Improving the performance of the team necessitates the operators to focus on the system, the surrounding environment, and situational awareness about the system states. Collaboration of team members is critical when employing the digital operating systems as the acute awareness of the overall progress of the group and individual activities of the operators drives the team to the desired end-states of the plant operations. Out-of-the loop (OOTL) performance problems can be referred to as the lack of supervisory control, system monitoring, and information processing in the digital HMIs. Under some circumstances Senior Reactor Operators (SRO) struggle to follow the actions of other Reactor Operators (RO) because with the new digital interfaces SROs lose track of how the procedures are carried out by ROs, which results in the loss of three way communication. Mutual awareness plays a significant role in keeping the SROs in the loop of plant operations. This paper will investigate, through literature review and expert interviews, the impact and human errors attributed to the increasing complexity of new digital main control rooms. Comparison of the new digital interfaces with the analog interfaces in nuclear power plants has the potential to shed light on increasing mutual awareness and three way communications between the SROs and ROs. Further, identification of the human errors and human actions can provide beneficial information, suggesting significant and critical improvements that can be implemented in the main control rooms to increase safety and reliability of new technology. Additionally, insights on mutual awareness for analog vs. digital control rooms can serve to improve human reliability.

99 GENERAL AND MISCELLANEOUS↗

Size-resolved ice nucleating particle (INP) concentrations from the MOSAiC campaign

Sample collection: Ground-based immersion mode ice nucleation measurements were conducted on samples collected at the U.S. DOE ARM AMF2 onboard the R/V Polarstern P-deck during MOSAiC from 23 Oct 2019 to 01 Oct 2020. Samples were collected using a Davis Rotating-drum Unit for Monitoring (DRUM; DRUMAir, LLC) cascading impactor in 4 stages (i.e., size bins). Instrument information on the model used, the DA400, can be found at the DRUMAir website (http://www.drumair.com/). Stages A, B, C, and D had particle size cuts at 2.96, 1.21, 0.34, and 0.15 &micro;m in particle diameter, respectively. Strips of sample substrate (PFA, perfluoroalkoxy; McMaster) were adhered to each disc in each stage and coated with petrolatum to enable particles to stick on impact. The discs move slowly over time, such that aerosol loading is streaked onto the PFA. Every 24 hours, a blank is created on the PFA to separate daily samples. During MOSAiC, the pump typically pulled 20 &ndash; 35 lpm (average of 32 lpm) at the inlet with all the discs in place in each stage. Daily samples were collected for each of the four stages, equaling 28,800 &ndash; 50,400 total L of air (average of 46,080 L total L of air) per sample. Discs rotated for approximately 24 days before the sampling per strip was complete and changed to a new disc. Discs were stored frozen after collection. Sample preparation for analysis: Following collection and transport from MOSAiC to the Kreidenweis/DeMott laboratory, care of PI Creamean, in Fort Collins, CO, strips were removed from the discs and cut up to separate daily samples. Each daily portion of the strip was stored in separate sterile 15-mL centrifuge tubes and stored in a standard commercial freezer until analysis (approximately 4 &ndash; 12 months after collection). Sample analysis: Drop freezing assays (DFAs) were conducted on one daily sample set every 3 &ndash; 4 days of the study for all four stages using the Colorado State University (CSU) cold plate (CP). Sample set days were chosen to cover the entire year of MOSAiC and avoiding days where the AOS purge system was activated. Immediately after removal from the freezer, 2 mL of 0.1-&micro;m filtered deionized water was added with a single-use sterile syringe directly to the tubes, then mixed using a vortex mixer for at least 30 minutes at 500 rpm to resuspend particles from the PFA into the deionized water. Following sample preparation, another single-use syringe was used to create 100 aliquots on a 3-inch diameter copper plate, then covered with a transparent plastic dome. The drops were approximately the same volume (2.5 &micro;L each), though careful inspection. The plate was cooled at approximately 3 &ndash; 10 K min-1 from room temperature until all drops froze on the plate. Temperature was measured by an Omega thermocouple probes and meter. Drop freezing was visually detected but recorded through monitoring software to provide the time frozen, channel of the meter used, temperature, and cooling rate for each drop. Not all 100 drops were always detected; the total number of rows in each data file equals the number of drops recorded, which typically was > 80 %. Each sample was tested three times (i.e., a new set of 100 drops was created for each test). Data provided are files containing these parameters. From the freezing temperatures recorded, the fraction frozen was and INP concentrations were calculated per L of air using the equation from Vali (1971): [INPs] = &minus;(ln(f)/Valiquot)*(Vsuspension/Vair) where f is the proportion of droplets not frozen, Valiquot is the volume of each aliquot (i.e., 2.5e-6 L), Vsuspension is the volume of the suspension (i.e., 0.002 L), and Vair is the volume of air per sample (i.e., 36000 L). Data usage guidance: There are three files included with this dataset: (1) dumcp_inp_concentrations.csv which includes cumulative INP concentrations binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C for all samples and size ranges (i.e., DRUM stages) processed via DFA using the CSU CP; (2) dumcp_inp_standard_deviation.csv which includes the standard deviations for all samples and temperatures in dumcp_inp_concentrations.csv; and (3) dumcp_blank_concentrations_standard_deviation.csv which includes the INP concentrations and standard deviations for the deionized water and PFA blanks binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C. The first column in each file represents the freezing temperature in &deg;C binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C (first cell in this column = header = &ldquo;temp&rdquo;). The second column in each file represents the standard deviation of each temperature bin in &deg;C (first cell in this column = header = &ldquo;temperr&rdquo;). For file (1) dumcp_inp_concentrations.csv, the remaining columns indicate the INP concentrations per L of air for each temperature bin. For each sample column, the header in the first row indicates the stage (i.e., size bin as &ldquo;A_&rdquo;, &ldquo;B_&rdquo;, &ldquo;C_&rdquo;, or &ldquo;D_&rdquo;) and date (&ldquo;_yyyymmdd) of collection. For file (2) dumcp_inp_standard_deviation.csv, the remaining columns indicate the INP concentration standard deviation per L of air for each temperature bin. For each sample column, the header in the first row indicates the stage (i.e., size bin as &ldquo;A_&rdquo;, &ldquo;B_&rdquo;, &ldquo;C_&rdquo;, or &ldquo;D_&rdquo;) and date (&ldquo;_yyyymmdd) of collection. For file (3) dumcp_blank_concentrations_standard_deviation.csv, the remaining columns indicate the INP concentrations and standard deviations for the deionized water blank (&ldquo;di_0.1&rdquo; and &ldquo;di_0.1sd&rdquo; for the concentrations and standard deviations, respectively) and PFA blank (&ldquo;pfa&rdquo; and &ldquo;pfasd&rdquo; for the concentrations and standard deviations, respectively). Please see Creamean et al. (2019a; 2018a; 2018b; 2019b) for additional details on DRUM sample collection and offline processing. For questions or help with data interpretation, contact Dr. Jessie Creamean (jessie.creamean@colostate.edu). References: Creamean, J. M., Mignani, C., Bukowiecki, N., and Conen, F.: Using spectra characteristics to identify ice nucleating particle populations during winter storms in the Alps, Atmos. Chem. Phys., 19, 8123&ndash;8140, 2019a. Creamean, J. M., Kirpes, R. M., Pratt, K. A., Spada, N. S., Maahn, M., de Boer, G., Schnell, R. C., and China, S.: Marine and terrestrial influences on ice nucleating particles during continuous springtime measurements in an Arctic oilfield location, Atmospheric Chemistry and Physics, 18, 18023-18042, 10.5194/acp-18-18023-2018, 2018a. Creamean, J. M., Primm, K. M., Tolbert, M. A., Hall, E. G., Wendell, J., Jordan, A., Sheridan, P. J., Smith, J., and Schnell, R. C.: HOVERCAT: A novel aerial system for evaluation of aerosol-cloud interactions, Atmospheric Measurement Techniques, 11, 3969-3985, 10.5194/amt-11-3969-2018, 2018b. Creamean, J. M., Cross, J. N., Pickart, R., McRaven, L., Lin, P., Pacini, A., Hanlon, R., Schmale, D. G., Ceniceros, J., Aydell, T., Colombi, N., Bolger, E., and DeMott, P. J.: Ice nucleating particles carried from below a phytoplankton bloom to the Arctic atmosphere, Geophys. Res. Lett., 46, 8572-8581, https://doi.org/10.1029/2019GL083039, 2019b. Vali, G.: Quantitative Evaluation of Experimental Results an the Heterogeneous Freezing Nucleation of Supercooled Liquids, Journal of the Atmospheric Sciences, 28, 402-409, 10.1175/1520-0469(1971)028<0402:qeoera>2.0.co;2, 1971.

4-stage DRUM aerosol collector (DA400),Ice nuclei,↗