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At least 1,153 records · Page 64

Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)

When the nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The lower Mississippi River basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System Model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and the leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate–nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region.

54 ENVIRONMENTAL SCIENCES↗

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

17 WIND ENERGY↗

Investigating Characteristic Droplet Size Distributions in Large Eddy Simulations of Stratocumulus Clouds

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large‐Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar behavior. Compared to observations, the simulated clouds are much more uniform. Analysis of the LES results suggests a connection to the local entrainment rate, so the poorly resolved entrainment interface in LES may be a cause of the uniformity. The uniformity of the large‐scale forcing could also be a factor.

cloud droplet size distributions↗

The B737 MAX 8 Accidents as Operational Experiences With Automation Transparency

Automation transparency has come into prominence in the human-machine systems literature, with researchers offering definitions, models, design frameworks, and empirical findings. However, missing from the literature are reflections on the actual operational experiences of human crews interacting with intelligent automation conceived from multiple transparency perspectives. The Boeing 737 MAX 8 accidents present a tragic case of a catastrophic failure of automation transparency. We explore this case with an emphasis on extracting design and regulatory insights for the nuclear power and other safety-critical domains.

99 GENERAL AND MISCELLANEOUS↗

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R 2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. Further, the key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

54 ENVIRONMENTAL SCIENCES↗

A general mechanistic framework for cross-scale understanding of hot spots and hot moments in carbon and water fluxes

Semi-arid ecosystems, like those in the American Southwest, exert a massive impact on the interannual variability of carbon and water cycling. Unfortunately, these carbon and water fluxes are notoriously difficult to predict due to their high spatial and temporal variability, which is poorly captured by the current generation of vegetation models. Indeed, this region is exemplified by the ‘hot spots and hot moments’ concept, which states that small areas in space (‘hot spots’) and transient moments in time (‘hot moments’) exert an outsized influence on biogeochemical cycling. However, the factors that regulate these pulses in biogeochemical activity are unknown, as is their variability across space and time. These uncertainties severely limit efforts to better represent hot spots and hot moments in models. Here, we seek to develop a generalized method for detecting and quantifying the importance of hot spots and hot moments from individual plant to regional scales. Underpinning this method is our recently developed statistical approach for identifying hot spots and hot moments. By applying this method to semi-continuous measurements of plant water status, a depth profile of soil water potential, and ecosystem fluxes via eddy covariance, we will track the fate of water through the soil-plant-atmosphere continuum and identify the mechanistic drivers of these transient pulses in biogeochemical activity. Then, we will expand this approach across a broad network of Ameriflux towers, and apply a machine learning approach that will allow us to upscale measurements of hot spots and hot moments across the American Southwest and quantify their impact on carbon and water cycles. These products will allow us to identify hot spots and hot moments across spatio-temporal scales and will serve as crucial data sources for validating a new generation of models that can better capture highly dynamic carbon and water fluxes. The proposed method will be easily transferable across biomes and will serve as a framework for future research on hot spots and hot moments across the plant ecophysiology, biometeorology, and vegetation modeling communities.

54 ENVIRONMENTAL SCIENCES↗

From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

60 APPLIED LIFE SCIENCES↗

Theoretical analysis of key factors achieving reversed magnetic shear q -profiles sustained with lower hybrid waves on EAST

The phase space analysis technique is applied to provide new insights into the fully non-inductive lower hybrid current driven (LHCD) discharges on EAST. The analysis shows that there are bounded and unbounded topologies of lower hybrid (LH) waves in phase space. For typical parameters on EAST, the propagation domain for 4.6 GHz LH waves is bounded, while unbounded for 2.45 GHz LH waves and one of the conditions is recognized to be good for achieving an off-axis current profile driven by 4.6 GHz LH waves on EAST. The parametric analysis on the potential power deposition (PPD) region for those experiments demonstrates that the reversed magnetic shear dominates the wave behavior, and confines the LH power absorption to the far off-axis region as long as it occurs in the 4.6 GHz dominated LHCD discharge. GENRAY/CQL3D simulations also confirm this effect of reversed shear on the power absorption of 4.6 GHz waves. A bounded propagation domain and a positive feedback loop between magnetic shear reversal and off-axis LHCD profile could explain the LH-only-sustained strong reversed magnetic shear observed in experiments on multiple machines. In contrast, there is no such effect for 2.45 GHz waves on EAST since the PPD region is less sensitive to the change of reversed shear due to the unbounded propagation domain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Abstract This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1116‐4 ]. In particular, the predictor now features not only the “disruption score,” as an indicator of the probability of an imminent disruption, but also a “sensitivity score” in real time to indicate the underlying reasons for the imminent disruption. This adds valuable physics interpretability for the deep learning model and can provide helpful guidance for control actuators now implemented into a modern plasma control system (PCS). The advance is a significant step forward in moving from modern deep learning disruption prediction to real‐time control and brings novel AI‐enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII‐D vetted in the earlier NATURE publication. In addition to “when” a shot is predicted to disrupt, this paper addresses reasons “why” by carrying out sensitivity studies. FRNN is accordingly extended to use more channels of information, including measured DIII‐D signals such as (i) the “n1rms” signal that is correlated with the n = 1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics; (ii) the bolometer data indicative of plasma impurity control; and (iii) “q‐min”—the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗

SU-F-P-39: End-To-End Validation of a 6 MV High Dose Rate Photon Beam, Configured for Eclipse AAA Algorithm Using Golden Beam Data, for SBRT Treatments Using RapidArc

Purpose: To use end-to-end testing to validate a 6 MV high dose rate photon beam, configured for Eclipse AAA algorithm using Golden Beam Data (GBD), for SBRT treatments using RapidArc. Methods: Beam data was configured for Varian Eclipse AAA algorithm using the GBD provided by the vendor. Transverse and diagonals dose profiles, PDDs and output factors down to a field size of 2×2 cm2 were measured on a Varian Trilogy Linac and compared with GBD library using 2% 2mm 1D gamma analysis. The MLC transmission factor and dosimetric leaf gap were determined to characterize the MLC in Eclipse. Mechanical and dosimetric tests were performed combining different gantry rotation speeds, dose rates and leaf speeds to evaluate the delivery system performance according to VMAT accuracy requirements. An end-to-end test was implemented planning several SBRT RapidArc treatments on a CIRS 002LFC IMRT Thorax Phantom. The CT scanner calibration curve was acquired and loaded in Eclipse. PTW 31013 ionization chamber was used with Keithley 35617EBS electrometer for absolute point dose measurements in water and lung equivalent inserts. TPS calculated planar dose distributions were compared to those measured using EPID and MapCheck, as an independent verification method. Results were evaluated with gamma criteria of 2% dose difference and 2mm DTA for 95% of points. Results: GBD set vs. measured data passed 2% 2mm 1D gamma analysis even for small fields. Machine performance tests show results are independent of machine delivery configuration, as expected. Absolute point dosimetry comparison resulted within 4% for the worst case scenario in lung. Over 97% of the points evaluated in dose distributions passed gamma index analysis. Conclusion: Eclipse AAA algorithm configuration of the 6 MV high dose rate photon beam using GBD proved efficient. End-to-end test dose calculation results indicate it can be used clinically for SBRT using RapidArc.

60 APPLIED LIFE SCIENCES↗

Estimation of Dose from Potential Radionuclide Emissions from the Sample Preparation Laboratory at the Materials and Fuels Complex

The Sample Preparation Laboratory (SPL) at the Materials and Fuels Complex will perform sample preparation, examination, and mechanical-property testing of highly irradiated materials. The purpose of this report is to document calculation of the estimated dose at public receptor locations from potential radionuclide releases to the atmosphere from SPL. Atmospheric dispersion and dose calculations were performed with the computer model CAP88-PC Version 4 in accordance with the requirements of Code of Federal Regulations (CFR), Title 40, “Protection of the Environment,” Part 61, “National Emission Standards for Hazardous Air Pollutants (NESHAPs),” Subpart H, “National Emission Standards for Emissions of Radionuclides Other than Radon from Department of Energy Facilities” (40 CFR 61, Subpart H 2010). Emission potentials were calculated based on the methodology in 40 CFR Part 61, Appendix D and additional guidance/approval by EPA Region 10 [see letter from Donald Dossett (EPA Region 10) to Tim Safford (DOE-ID), Oct 19, 2017 (CCN 241475)] for solid materials that undergo heating. Estimates of total effective dose are based on low-level chronic exposure. The potential maximum dose to a member of the public was calculated to be 0.02 mrem/year based on an estimated 16 g of the 30 kg source term (0.053%) being exposed to very high temperatures associated with electrical-discharge machining (EDM) and the remaining material heated to 100°C. Calculations show the maximum dose could be as high as 0.05 mrem/year if the remaining material were heated to 1400°C (i.e., the melting point of stainless steel). The potential maximum dose to a public receptor from emissions at SPL is not expected to exceed 0.1 mrem/year (1% of the standard) so long as the material impacted by EDM does not exceed 56 g (3.5 times the expected mass of 16 g and less than 0.2% of the total source term), and the remaining material is not heated to temperatures greater than 1400°C. It should be noted that credit for HEPA filtration does not impact the dose results because the dose is dominated by radionuclides that have exceeded their boiling point; thus, no credit for HEPA filtration may be applied to the emission factor.

99 GENERAL AND MISCELLANEOUS↗

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗

Development of SRF monolayer/multilayer thin film materials to increase the performance of SRF accelerating structures beyond bulk Nb

The minimization of cost and energy consumption of future particle accelerators, both large and small, depends upon the development of new materials for the active surfaces of superconducting RF (SRF) accelerating structures. SRF properties are inherently a surface phenomenon as the RF only penetrates the London penetration depth ?, typically between 20 and 400 nm depending on the material. The greatest potential for dramatic new performance capabilities lies with methods and materials which deliberately produce the sub-micron-thick critical surface layer in a controlled way. Thus, two opportunities arise for the use of SRF thin ?lms as single layer superconductor or multilayer Superconductor-Insulator-Superconductor structures: SRF properties are inherently a surface phenomenon, and when other technological processes are optimized, the fundamental limit to the maximum supportable RF ?eld amplitude is understood to be the ?eld at which the magnetic ?ux ?rst penetrates into the surface, Hc1. Niobium, the material most exploited for SRF accelerator applications, has Hc1 ? 170 mT, which yields a maximum acceler?ating gradient of less than 50 MV/m. Thus, new materials must be investigated to push this limit to the desired range. Although many compounds have shown higher critical superconducting transition temperature (Tc), their critical ?eld (Hc1) is lower than niobium. To circumvent this issue, a Super?conductor/Insulator/Superconductor (S-I-S) multilayer ?lms approach has been proposed to achieve performance in excess of that of bulk Nb. In this system, the critical ?eld is enhanced and the ?uxon penetration delayed by using, on top of the Nb surface, a superposition of superconductor layers, each less than a penetration depth in thickness, separated by a dielectric layer a few nanometers thick. For bulk type-II superconducting materials, vortex entry occurs at surface magnetic ?eld Hc1. The key aspect of Gurevich's analysis is the recognition that vortex entry is inhibited if the surface layer thick?ness (d) of the superconductor is less than the penetration depth, ?. The overlayer provides magnetic screening of the underlying Nb with the attenuation factor exp(d/ ?0). The Nb surface can remain in the Meissner state at ?elds much higher than in bulk due to the increase of the parallel Hc1 in a thin ?lm. This could potentially lead to further improvement in RF cavity performance using the bene?t of the higher-Tc superconductors without being limited by their lower Hc1. Niobium on copper (Nb/Cu) technology for superconducting cavities has proven over the years to be a viable alternative to bulk niobium. However the deposition techniques used for cavities, mainly magnetron sputtering, have not yielded, so far, SRF surfaces suitable for high ?eld performance. High quality ?lms can be grown using methods of energetic condensation, such as Electron Cy?clotron Resonance (ECR) Nb ion source in UHV and High Power Impulse Magnetron Sputtering (HiP?IMS) with energetic ions only and neutral atoms signi?cantly blocked. The main advantages of these techniques are the production of higher ?ux of ions with controllable incident angle and kinetic energy and the absence of macroparticle production. The project in general, is focusing onto understanding the physics underlying the RF superconduc?tivity in layered structures and developing SRF material ?lms suitable as substrate and S-I-S multilayer structures. In ?rst hand, the primary focus is to understand the physics governing the relationship be?tween growth conditions, ?lm microstructure and RF performance for SRF materials produced by energetic condensation. The deposition by energetic condensation in UHV via ECR and HiPIMS are studied for Nb and B1 superconducting compounds on substrates with various lattice match and sur?face conditions (in-situ etching, high energy assisted atomic annealing, deposition temperature). The general energy-temperature zone model developed by André Anders is explored to de?ne the optimized coating conditions for ?lms suitable for single layer and multilayer SRF structures. The contribution of both the ion incident energy and the substrate lattice energy on the nucleation of the SRF materials considered, and some insulating materials (AlN and MgO) and the in?uence of the coating conditions on the resulting materials, superconducting and/or RF properties will be investigated. Attention is be given to minimize the residual stress (intrinsic and thermal) with the choice of lattice match between the ?lm and substrate materials, the coating temperature, and to evaluate the coating methods used in terms of conformality for 3D structures. In second hand, as a proof of concept, the coating condi?tions developed will be used to prepare multi-layer superconductor/insulator/superconductor (S-I-S) ?lms having high quality factor and high gradient breakthrough potential. A thick Nb ?lm (> 1µm) could be used as a substrate. Particular attention is given to the interface between superconducting and insulator ?lms. For this work, various material analyses techniques have been used to character?ize each type of thin ?lm material: electron backscatter di?raction (EBSD), X-ray di?raction (XRD), high-resolution scanning electron microscopy (SEM), transmission electron microscopy (TEM), stylus and AFM pro?lometry, secondary ion mass spectroscopy (SIMS), scanning auger microscopy (SAM). Superconducting and RF measurements are also performed (RRR, Tc, and low-temperature SRF sur- face impedance, SQUID-magnetometry). The insight gained from this project on the physics involved in RF superconductivity of layered structures open the way to enable major reductions in both capital and operating costs associated with future particle accelerators across the spectrum from low foot?print compact machines to energy frontier facilities. Realization of quality thin ?lm SRF cavities may dramatically change the cost structure of acceleration systems and signi?cantly improve performance consistency and reliability.

VALENTE-FELICIANO, Anne-Marie↗

Using convolutional neural networks to accelerate three-dimensional coherent synchrotron radiation computations

Calculating the effects of coherent synchrotron radiation (CSR) is one of the most computationally expensive tasks in accelerator physics. Here, we use convolutional neural networks (CNNs), along with a latent conditional diffusion (LCD) model, trained on physics-based simulations to speed up calculations. Specifically, we produce the 3D CSR wakefields generated by electron bunches in circular orbit in the steady-state condition. Two datasets are used for training and testing the models: wakefields generated by three-dimensional Gaussian electron distributions and wakefields from a sum of up to 25 three-dimensional Gaussian distributions. The CNNs are able to accurately produce the 3D wakefields ∼250–1000 times faster than the numerical calculations, while the LCD achieves a gain of a factor of ∼34. We also test the extrapolation and out-of-distribution generalization ability of the models. They generalize well on distributions with larger spreads than what they were trained on but struggle with smaller spreads.

43 PARTICLE ACCELERATORS↗

Binding profiles for 961 Drosophila and C. elegans transcription factors reveal tissue-specific regulatory relationships

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here, we present the culmination of the efforts of the modENCODE (model organism Encyclopedia of DNA Elements) and modERN (model organism Encyclopedia of Regulatory Networks) consortia to systematically assay TF binding events in vivo in two major model organisms,Drosophila melanogaster(fly) andCaenorhabditis elegans(worm). These data sets comprise 605 TFs identifying 3.6 M sites in the fly and 356 TFs identifying 0.9 M sites in the worm, and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed “metapeaks,” that larger metapeaks have characteristics of high-occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single-cell RNA-seq data in a machine-learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent–daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing green fluorescent protein (GFP)-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell-type-specific TF–target relationships.

Biochemistry & Molecular Biology↗

Enhancement of operational Flexibility of Power Plants Using IN740

Headers are crucial components within diverse industries, especially in the energy sector, as they enable the efficient transfer of fluids. The selection of materials for headers is determined by their specific applications; for example, Grade 91 and Grade 92 steels are commonly utilized. In our research, we recommend the adoption of INCONEL alloy 740 due to its exceptional robustness and heat-resistant characteristics. One critical parameter in header systems is the heat transfer coefficient, which directly influences the efficiency of heat exchange processes and can thereby impact the structural integrity of the headers. This coefficient is closely linked with factors such as the Nusselt number, which is influenced by fluid flow characteristics and thermal properties. The flow direction within headers, whether unidirectional or multidirectional—significantly affects the overall dynamics of heat transfer. This research focuses on exploring the behavior of headers, a specific pipeline system component, using ANSYS simulation software. The study aims to predict heat transfer and mechanical behavior within headers under various conditions through steady-state and transient simulations (parts 1 and 2 of the report). Key parameters such as heat transfer coefficient, velocity, and temperature are examined with the goal of optimizing header design. Part 3 of the report addresses the critical yet underexplored relationship between pressure drop and heat transfer coefficients in the transient flow regime within headers, specifically focusing on smooth horizontal circular tubes. Limited experimental work has been conducted in this area, prompting the need for comprehensive analysis. By leveraging machine learning techniques, this research aims to establish a correlation between pressure drop and heat transfer across various flow conditions, including laminar, transient, quasi-turbulent, and turbulent regimes. The data utilized for this analysis were meticulously gathered from existing literature, capturing simultaneous measurements of pressure drops and heat transfer. Part 4: In the context of power plants, header pipes are essential components that significantly influence system performance by facilitating the collection and distribution of steam. This report highlights the critical role of header pipes in enhancing reliability, efficiency, and overall power plant performance. A key aspect of this investigation is shape optimization, which aims to maximize performance while minimizing material usage. By focusing on shape optimization, this research contributes to improved efficiency and a reduced environmental footprint for power plant installations. The methodology developed in this study emphasizes optimizing header shapes to decrease reliance on expensive alloy materials and lower maintenance costs. Furthermore, a case study was conducted using a header from an operational power plant to validate the proposed optimization techniques. Finally, part 5 addresses the challenges associated with flexible operations in boiler systems, the study outlines several strategies for enhancing the durability and reliability of steam headers. Key approaches include material selection, which involves utilizing advanced materials with superior high-temperature properties and enhanced fatigue resistance to extend the lifespan of steam headers. Design modifications are also recommended, focusing on implementing changes that mitigate thermal stress and cyclic loading to reduce the likelihood of failures under varying operating conditions. Additionally, the study emphasizes the importance of regular inspection and monitoring by establishing rigorous protocols to detect early signs of damage, allowing for timely maintenance and minimizing the risks of catastrophic failures. Furthermore, operational guidelines are developed to minimize the frequency and severity of thermal transients, ensuring stable and efficient operations. Collectively, these strategies provide a robust framework for overcoming the unique challenges posed by flexible operation in boiler systems, ultimately contributing to improved reliability and performance in power plant operations. Through this comprehensive approach, the study not only enhances the understanding of pressure drop and heat transfer relationships but also promotes advancements in design and operation that will benefit the entire power generation industry.

01 COAL, LIGNITE, AND PEAT↗

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.

36 MATERIALS SCIENCE↗