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At least 685 records · Page 38

Achieving 5,000-h and 8,000-h Low-PGM Electrode Durability on Automotive Drive Cycles

Whereas total Pt loading in anode and cathode catalysts below 0.125 mg cm −2 is required to meet the stringent cost target for automotive fuel cell systems (FCS) for light duty vehicles, low-loaded cathode catalysts are susceptible to unacceptable aging-related performance losses at high current densities. A framework model, validated by accelerated stress test data, has identified cell voltage, relative humidity (RH) and temperature as the key operating variables that affect degradation of a high-activity d-PtCo/C cathode catalyst with 0.1 mg cm −2 Pt loading. Drive cycle simulations indicate that these can be controlled by properly selecting the minimum FCS power, compressor-expander module (CEM) turndown, and stack coolant temperature. The optimum system parameters are 4-kW e minimum power for an 80-kW e FCS, CEM turndown of 12.5, and 66 °C average coolant exit temperature that combine to limit the maximum cell voltage to 850 mV and outlet RH to 90%–100%. Depending on Pt loading, the mismatch between actual and allowable degradation for 10% power loss over 5,000-h lifetime requires the stack to be oversized by 2.4%–5%, resulting in 8.4%–41% lower Pt utilization and 7.1%–20.5% penalty in stack cost. The corresponding results for 8,000-h lifetime are 10.3%-14% stack oversizing, 23%–51.8% lower Pt utilization, and 24.1%–35.4% stack cost penalty.

08 HYDROGEN↗

Interseasonal Connections between the Timing of the Stratospheric Final Warming and Arctic Sea Ice

Connections across seasons in atmospheric circulation and sea ice have long been sought to advance seasonal prediction. This study presents a link between the springtime stratosphere and Arctic sea ice in summer through autumn. The polar stratospheric vortex dominates the winter stratosphere before breaking down each spring, which is called the stratospheric final warming, as solar radiation returns to the pole. Interannual variability of this breakdown is dynamically driven, leading to different springtime tropospheric and surface circulation patterns. To examine the different impacts of delayed and early final warmings, a multimodel composite was generated from selected CMIP5 models. Additionally, regressions were performed on JRA-55 against an index of springtime polar vortex strength. In both the multimodel composites and reanalysis regressions, significant anomalies in sea ice thickness persist several months following an anomalous timing of the final warming. A later final warming or stronger springtime polar stratospheric vortex leads to negative sea ice thickness anomalies in the East Siberian Sea and positive anomalies in the Beaufort Sea in comparison with an earlier final warming or weaker polar vortex. The spring polar stratospheric vortex is related to spring polar surface circulation patterns. The winds associated with this pattern induce anomalous sea ice motion, moving ice from the East Siberian Sea toward the Beaufort Sea. Reduced sea ice in the East Siberian Sea is linked to anomalous warmth over this region in autumn. Our results suggest that the timing of the stratospheric final warming exerts an influence on the tropospheric circulation and sea ice through autumn, which has implications for seasonal climate prediction.

Kelleher, Michael↗

The association between high birth weight and the risks of childhood CNS tumors and leukemia: an analysis of a US case-control study in an epidemiological database

Background: High birth weight (BW), 4000 g or larger, is an established risk factor for childhood leukemia. However, its association with central nervous system (CNS) tumor risk is yet unclear. The present study examined it, analyzing data obtained from a case-control study conducted among three states from the US. The association with childhood leukemia risk was also further examined. Methods: In this study, a data set provided by the Comprehensive Epidemiologic Data Resource was analyzed with an official permission. The original case-control study was conducted to examine the association between paternal preconception exposure to ionizing radiation and childhood cancer risk. Cases with childhood cancer were mainly ascertained from local hospitals, and controls were selected, matched with birth year (1-year category), county of residence, sex, ethnicity and maternal age (+/-2 years). Since the ID numbers were unavailable, conventional logistic analyses were conducted adjusting for those matching variables except for the county of residence. In addition to those variables, gestational age, age at diagnosis and study sites as covariables were included in the logistic models. Results: Analyzed subjects were 72 CNS tumor cases, 124 leukemia cases and 822 controls born from 1945 to 1989. The odds ratios (ORs) of CNS tumor risk for children with low BWs (<2500 g) and high BWs (>4000 g) were 2.0 (95% confidence interval [CI]) = 0.7, 5.9) and 2.5 (95%CI = 1.2, 5.2)], respectively. When high-BW children were restricted to those who were large for gestational age (LGA), the OR for high-BW children remained similar (OR = 2.7; 95%CI = 1.1, 6.2). On the other hand, the ORs of leukemia risk for children with low and high BWs were 0.8 (95%CI = 0.2, 3.0) and 1.4 (95%CI = 0.7, 2.6), respectively. In the normal range of BW (2500–4000 g), higher BW was positively associated with CNS tumor risk (beta = 0.0011, p for trend = 0.012). However, the association with leukemia risk was not significant (beta = -0.0002, p for trend = 0.475). Conclusion: High-BW and LGA children had an elevated childhood CNS tumor risk. In the normal BW range, the BW itself was positively related to CNS tumor risk. No significant association between BW and childhood leukemia risk was observed in this study.

60 APPLIED LIFE SCIENCES↗

Performance Evaluation of Three RTU Energy Efficiency Technologies

This project was part of an effort by CLEAResult and ComEd to evaluate the energy saving potential of emerging technologies in the Chicago area. This project focused on evaluating emerging technologies related to rooftop units (RTUs). An RTU with a single-stage compressor and a constant-speed fan with an induction motor was selected as the baseline for technology comparison. Three retrofit strategies were investigated. The first two involved replacing the single-stage compressor of an RTU by either a two-stage or variable-speed compressor and adding a variable frequency drive (VFD) to the constant speed fan. The use of a multi-/variable-stage compressor improves the part-load efficiency of the compressor which will ultimately result in annual energy savings as well as peak demand shaving in some cases where the design capacity of the RTU is larger than the maximum cooling load of the building space that it is serving. The third technology was the use of a high rotor pole switched reluctance motor (SRM) as a replacement for the constant-speed supply fan. The SRM was applied in single-speed, two-stage and variable-speed compressor RTUs. SRM motors run via reluctance torque. Their stator poles are driven by direct current (DC) power and require an inverter as well as active control when using alternating current (AC) power. This inherent property results in high efficiency over a range of operating conditions. It also exhibits higher efficiency compared to variable frequency drives (VFDs) since its switching frequency is much slower (SCE, 2018).The three technologies investigated are summarized below: 1) Replacing the single-speed compressor with a two-stage compressor and adding a VFD to the supply fan. 2)Replacing the single-speed compressor with a variable-speed compressor and adding a VFD to the supply fan. 3) Replacing the constant-speed induction motor of the supply fan with a high rotor pole SRM. DOE’s building simulation platform EnergyPlus and its graphical user interface OpenStudio were used to evaluate the energy-saving potential of upgrading RTUs, by leveraging experimental data from previous research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The Prospects for Pumped Storage Hydropower in Alaska

Key Takeaways: The resource mapping analysis confirmed that numerous locations in Alaska are suitable for the development of pumped storage hydropower (PSH) projects, both larger grid scale projects and smaller projects that could be suitable for remote communities; The resource assessment for larger, grid-scale projects showed the potential for more than 1,800 closed-loop systems in Alaska, with a total energy storage capacity of about 4 terawatt hours (TWh); Because of their small reservoir sizes and dam heights, many locations were identified as potentially suitable for small-scale PSH systems. Nearly 50% of the identified potentially suitable small-scale PSH sites are in Southeast Alaska; PSH candidate sites were part of the optimal capacity expansion solution in all scenarios analyzed for the Railbelt system. Depending on the scenario, the new PSH capacity that the model selected for the analysis period until 2046 ranged from 300 MW to 600 MW. The locations and timing of new PSH investments vary in different scenarios; Lithium-ion batteries were also selected a source of new generating capacity in all analyzed scenarios for the Railbelt system, indicating that the system will need a mix of short- and long-duration energy storage to support variable renewable energy sources and provide system reliability in the future; For rural communities, analysis results showed that PSH suitability is very site-specific; in addition to diesel fuel costs and PSH capital costs, suitability depends heavily on available renewable resources and existing infrastructure (e.g., reservoirs, transmission access and construction road access); The analysis for rural communities also showed that PSH projects with 10-hour energy storage are likely to be more economical for remote community applications in Alaska than those with larger reservoirs that could provide 10 days of energy storage; Lithium-ion batteries seem to be an economically more viable energy storage option for small, remote communities in Alaska.

13 HYDRO ENERGY↗

Drought adaptation index (DAI) based on BLUP as a selection approach for drought-resilient switchgrass germplasm

This study introduces a Drought Adaptation Index (DAI), derived from Best Linear Unbiased Prediction (BLUP), as a method to assess drought resilience in switchgrass (Panicum virgatum L.). A panel of 404 genotypes was evaluated under drought-stressed (CV) and well-watered (UC) conditions over four consecutive years (2019–2022). BLUP-estimated biomass yields were used to calculate the DAI, which enabled classification of genotypes into four adaptation groups: very well-adapted, well-adapted, adapted, and unadapted. The DAI was compared with conventional drought tolerance indices, including the Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), and Yield Stability Index (YSI). Correlation analyses demonstrated strong agreement between DAI and these indices, supporting its validity and consistency. Biplot analyses using the Genotype plus Genotype-by-Environment Interaction (GGE) and Additive Main Effects and Multiplicative Interaction (AMMI) models revealed significant genotype-by-environment interactions (GEI) and identified J222.A, J463.A, and J295.A. A as high-performing genotypes, with J222.A exhibiting greater yield stability across treatments and years. Additionally, DAI isoline curves provided a graphical representation of differential genotype performance under drought and control conditions. These visualizations aided in distinguishing genotypes with stable and superior biomass yield across contrasting environments. Overall, the BLUP-based DAI is a robust and practical selection tool that improves the accuracy of identifying drought-resilient, high-yielding switchgrass genotypes. Its integration into breeding programs offers a comprehensive framework for improving biomass productivity and stress adaptation under variable climatic conditions. The application of DAI supports the development of climate-resilient cultivars and contributes to sustainable bioenergy and forage production systems.

BLUP↗

Underestimation of column NO 2 amounts from the OMI satellite compared to diurnally varying ground-based retrievals from multiple PANDORA spectrometer instruments

Retrievals of total column NO 2 (TCNO 2 ) are compared for 14 sites from the Ozone Measuring Instrument (OMI using OMNO2-NASA v3.1) on the AURA satellite and from multiple ground-based PANDORA spectrometer instruments making direct-sun measurements. While OMI accurately provides the daily global distribution of retrieved TCNO 2 , OMI almost always underestimates the local amount of TCNO 2 by 50 % to 100 % in polluted areas, while occasionally the daily OMI value exceeds that measured by PANDORA at very clean sites. Compared to local ground-based or aircraft measurements, OMI cannot resolve spatially variable TCNO 2 pollution within a city or urban areas, which makes it less suitable for air quality assessments related to human health. In addition to systematic underestimates in polluted areas, OMI's selected 13:30 Equator crossing timepolar orbit causes it to miss the frequently much higher values of TCNO 2 that occur before or after the OMI overpass time. Six discussed Northern Hemisphere PANDORA sites have multi-year data records (Busan,Seoul, Washington DC, Waterflow, New Mexico, Boulder, Colorado, and Mauna Loa),and one site in the Southern Hemisphere (Buenos Aires, Argentina). The first four of these sites and Buenos Aires frequently have high TCNO 2 (TCNO 2 > 0.5 DU). Eight additional sites have shorter-term data records in the US and South Korea. One of these is a 1-year data record from a highly polluted site at City College in New York City with pollution levels comparable to Seoul, South Korea. OMI-estimated air mass factor, surface reflectivity, and the OMI 24 km × 13 km FOV (field of view)are three factors that can cause OMI to underestimate TCNO 2 . Because of the local inhomogeneity of NO x emissions, the large OMI FOV is the most likely factor for consistent underestimates when comparing OMI TCNO 2 to retrievals from the small PANDORA effective FOV (measured in m 2 ) calculated from the solar diameter of 0.5°.

47 OTHER INSTRUMENTATION↗

Fragmentation of jets containing a prompt J$/\psi$ meson in PbPb and pp collisions at $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV

Jets containing a prompt J$/\psi$ meson are studied in lead-lead collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV, using the CMS detector at the LHC. Jets are selected to be in the transverse momentum range of 30 $\lt p_\mathrm{T} \lt$ 40 GeV. The J$/\psi$ yield in these jets is evaluated as a function of the jet fragmentation variable $z$, the ratio of the J$/\psi$$p_\mathrm{T} $ to the jet $p_\mathrm{T}$. The nuclear modification factor, $R_\mathrm{AA}$, is then derived by comparing the yield in lead-lead collisions to the corresponding expectation based on proton-proton data, at the same nucleon-nucleon center-of-mass energy. The suppression of the J$/\psi$ yield shows a dependence on $z$, indicating that the interaction of the J$/\psi$ with the quark-gluon plasma formed in heavy ion collisions depends on the fragmentation that gives rise to the J$/\psi$ meson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Selective Se-for-S substitution in Cs-bearing uranyl compounds

Phase formation in the mixed sulfate-selenate aqueous system of uranyl nitrate and cesium nitrate has been investigated. Two types of crystalline compounds have been obtained and characterized using a number of experimental (single crystal XRD, FTIR, SEM) and theoretical (information-based complexity calculations, topological analysis) techniques. No miscibility gaps have been observed for Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}] (T= S, Se), which crystallizes in tetragonal system, P-42{sub 1}m, a =9.616(1)–9.856(2), c =8.105(1)–8.159(1) Å, V =749.6(2)–792.5(3) Å{sup 3}. Nine phases with variable amount of S and Se have been structurally characterized. The structures of the Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}] (T= S, Se) compounds are based upon the [(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}]{sup 2-} layers of corner-sharing uranyl pentagonal bipyramids and TO{sub 4} tetrahedra. The layers contain two types of tetrahedral sites: T1 (3-connected, i.e. having three O atoms shared by adjacent uranyl polyhedra) and T2 (4-connected). The Se-for-S substitution in tetrahedral sites is highly selective with smaller S{sup 6+} cation showing a strong preference for the more tightly bonded T2 site. Crystallization in the pure Se system starts with the formation of Cs{sub 2}[(UO{sub 2})(SeO{sub 4}){sub 2}(H{sub 2}O)](H{sub 2}O) crystals, its subsequent dissolution and formation of Cs{sub 2}[(UO{sub 2}){sub 2}(SeO{sub 4}){sub 3}]. The information-based structural complexity calculations for these two phases support the rule that more topologically complex structures form at the latest stages of crystallization. - Graphical abstract: Nine phases representing the Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}] (T= S, Se) solid solution series with variable amount of S and Se have been prepared by isothermal evaporation from aqueous solutions and characterized using a number of experimental and theoretical techniques. No immiscibility is observed between the pure sulfate and selenate compounds. The Se-for-S substitution in tetrahedral sites is highly selective with smaller S{sup 6+} cation showing a strong preference for the more tightly bonded 4-connected site. - Highlights: • Single crystals of novel mixed sulfate-selenate uranyl oxysalts were prepared by evaporation method. • Topological analysis and information-based complexity calculations were used for structure description. • The selective Se-for-S substitution was observed. • Evolution of phase formation in the aqueous Cs{sup +}–UO{sub 2}{sup 2+}–SO{sub 4}{sup 2–}–SeO{sub 4}{sup 2–} system was analyzed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Intelligent Weld Manufacturing: Role of Integrated Computational Welding Engineering

A master welder uses his sensory perceptions to evaluate the process and connect them with his/her knowledge base to take the necessary corrective measures with his/her acquired skills to make a good weld. All these actions must take place in real time. Success depends on intuition and skills, and the procedure is labor-intensive and frequently unreliable. The solution is intelligent weld manufacturing. The ultimate goal of intelligent weld manufacturing would involve sensing and control of heat source position, weld temperature, weld penetration, defect formation and ultimately control of microstructure and properties. This involves a solution to a problem (welding) with many highly coupled and nonlinear variables. The trend is to use an emerging tool known as intelligent control. This approach enables the user to choose a desirable end factor such as properties, defect control, or productivity to derive the selection of process parameters such as current, voltage, or speed to provide for appropriate control of the process. Important elements of intelligent manufacturing are sensing and control theory and design, process modeling, and artificial intelligence. Significant progress has been made in all these areas. Integrated computational welding engineering (ICWE) is an emerging field that will aid in the realization of intelligent weld manufacturing. The paper will discuss the progress in process modeling, microstructure, properties, and process control and automation and the importance of ICWE. Also, control and automation strategies for friction stir welding will be discussed.

42 ENGINEERING↗

Wettability variation and its impact on CO 2 storage capacity at the Wyoming CarbonSAFE storage hub: An experimental approach

Meeting global and national net zero carbon emission targets will require geologic carbon disposal. The U.S. Department of Energy (DOE) has accordingly funded significant research in this area, including the Wyoming CarbonSAFE project at Dry Fork Station (DFS) in Campbell County, Wyoming. This work studied wettability on micro- and macro-scales, CO 2 storage potential, and the correlation between the two to support the Wyoming CarbonSAFE project’s subsurface assessment. During the study, a target formation’s wettability was found to affect how much CO 2 can be stored in a given formation. Here, in this study, representative rock samples were selected from the target storage formations— Lakota, Hulett, and Minnelusa—based on the heterogeneity of the lithology, permeability, and porosity of the respective formations. The rock samples are all fine-grained sandstone with variable cementation and bedding structure, including different scales of laminated bedding. The porosity and permeability vary within the range of 9.0–14.3% and 0.1–28.9 mD, respectively. These rock samples were prepared for the micro-scale wettability (contact angle measurement), macro-scale wettability (wettability index derived from unsteady-state flow characterization for the core plugs), and CO 2 storage evaluation. The macro-scale experiments suggested that wettability appeared to dominate the CO 2 storage potential performance during the drainage process, where less water-wet behavior promoted higher CO 2 storage potential. The micro-scale wettability tests showed that the rock samples at the studied reservoir conditions behaved water-wet and became more water-wet as pressure increased. This kind of wettability change discourages further CO 2 storage potential yet benefits the CO 2 residual trapping as the CO 2 injection proceeds for the studied area. The results allow the recommendation of the best reservoir candidate for storage based on wettability that affects CO 2 storage. The work presented in this study provides valuable insights into wettability’s effect on the CO 2 storage capacity and wettability’s importance when identifying the optimal CO 2 storage formation to meet the project’s goals.

58 GEOSCIENCES↗

CatTestHub: A benchmarking database of experimental heterogeneous catalysis for evaluating advanced materials

The ability to quantitatively compare newly evolving catalytic materials and technologies is hindered by the widespread availability of catalytic data collected in a consistent manner. While certain catalytic chemistries have been widely studied across decades of scientific research, quantitative comparisons based on literature information is hindered by variability in reaction conditions, types of reported data, and reporting procedures. Here, we present CatTestHub, an open-access database dedicated to benchmarking experimental heterogeneous catalysis data. Combining systematically reported catalytic activity data for selected probe chemistries, with relevant material characterization and reactor configuration information, the database provides a collection of catalytic benchmarks for distinct classes of active site functionality. Through key choices in data access, availability, and traceability, CatTestHub seeks to balance the fundamental information needs of chemical catalysis and the FAIR data design principles. Details of the database architecture and the means through which to navigate it are presented, highlighting examples of catalytic insights readily drawn from the available benchmarking data. In its current iteration, CatTestHub spans over 250 unique experimental data points, collected over 24 solid catalysts, that facilitated the turnover of 3 distinct catalytic chemistries. Here, a roadmap is presented through which to expand the open-access platform that serves as a community wide benchmark, primarily through continuous addition of kinetic information on select catalytic systems by members of the heterogeneous catalysis community at large.

Benchmark↗

Electrocardiographic changes predate Parkinson’s disease onset

Autonomic nervous system involvement precedes the motor features of Parkinson’s disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.

59 BASIC BIOLOGICAL SCIENCES↗

Characterizing the effect of incrementally increasing dry bulb temperature on linear and nonlinear measures of heart rate variability in nonpregnant, mid-gestation, and late-gestation sows

Abstract Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. HRV is a commonly used noninvasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) nonpregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1–37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19–20.99, 21–22.99, 23–24.99, 25–26.99, 27–28.99, 29–30.99, 31–32.99, 33–34.99, and 35–36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r–r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.

Agriculture↗

Evaluation of Electron Tomography Capabilities for Shale Imaging

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. Furthermore, the limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

3D scanning transmission electron microscopy↗

Siting Analysis of a Solar-Nuclear-Desalination Integrated Energy System

Nuclear power is typically deployed as a baseload generator. Increased penetration of variable renewables motivates combining nuclear and renewable technologies into Integrated Energy Systems (IES) to improve dispatchability, component synergies and, through cogeneration, address multiple markets. However, combining multiple energy resources heavily depends on the proper selection of each system’s location and design limitations. In this paper, co-siting options for IES that couple nuclear and concentrating solar power (CSP) with thermal desalination are investigated. A comprehensive siting analysis is performed that utilizes global information survey data to determine possible co-siting options for nuclear and solar thermal generation in the United States. Viable co-siting options are distributed across the Southwestern U.S., with the greatest concentration of siting options in the southern Great Plains, although siting with higher solar direct normal irradiance is possible in other states such as Arizona and New Mexico. Brackish water desalination is also attractive across the southwest U.S. due to high water stress, but for brackish water desalination reverse osmosis (an electricity driven process) is most cost- and energy-efficient, which does not require co-siting with the thermal generator. The most attractive state for nuclear and thermal desalination (which is more attractive when using seawater) is Texas, although other areas may become attractive as water stress increases over the coming decades. Co-siting of all CSP and thermal desalination is challenging as attractive CSP sites are not coastal.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗