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At least 703 records · Page 39

SoDaH: the SOils DAta Harmonization database, an open-source synthesis of soil data from research networks, version 1.0

Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Synthesizing datasets collected by different research networks presents opportunities to expand the ecological gradients and scientific breadth of information available for inquiry. Synthesizing these data is challenging, especially considering the legacy of soil data that have already been collected and an expansion of new network science initiatives. To facilitate this effort, here we present the SOils DAta Harmonization database (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets from multiple research networks. SoDaH is built on several network science efforts in the United States, but the tools built for SoDaH aim to provide an open-access resource to facilitate synthesis of soil carbon data. Moreover, SoDaH allows for individual locations to contribute results from experimental manipulations, repeated measurements from long-term studies, and local- to regional-scale gradients across ecosystems or landscapes. Finally, we also provide data visualization and analysis tools that can be used to query and analyze the aggregated database. The SoDaH v1.0 dataset is archived and available at https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020).

54 ENVIRONMENTAL SCIENCES↗

Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint

A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗

How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment

The sequence of instantaneous driving decisions and its variations, known as driving volatility, can be a leading indicator of unsafe driving practices. The research issue is to characterize volatility in instantaneous driving decisions in longitudinal and lateral direction and to seek an understanding of how driving volatility relates to crash severity. By using a unique real-world naturalistic driving database from the SHRP 2, a test set of 671 crash events featuring around 0.2 million temporal samples of real-world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. The volatility indices are then linked with individual crash events including information on crash severity, drivers’ pre-crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an in-depth analysis is conducted using the aggregate as well as segmented (based on time to collision) real-world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter ordered models with heterogeneity in parameter means are estimated. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) prior to crash occurrence increases the likelihood of police reportable or severe crash events. Importantly, compared to the effect of volatility in longitudinal acceleration on crash outcomes, the effect of volatility in longitudinal deceleration is significantly greater in magnitude. Methodologically, the random parameter models with heterogeneity-in-means significantly outperformed both the fixed parameter and random parameter counterparts. The relevance of the findings to the development of proactive behavioral countermeasures for drivers is discussed.

Wali, Behram↗

Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels.We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.

Yoon, Hong-Jun↗

Embodied carbon in the U.S. manufacturing and trade

On average, one quarter of the global carbon footprint is embodied in traded goods. These emissions are a growing issue for global efforts to decarbonize the world economy. Embodied emissions in trade are not accounted for by existing greenhouse gas accounting systems. For example, countries only report their domestic carbon emissions (also known as production-based or territorial accounting) to the Intergovernmental Panel on Climate Change (IPCC). If the embodied carbon in trade were accounted for and reported, the promising climate trends depicted by many countries would be negated or reversed. For example, many achievements of reducing emissions by developed countries under the Kyoto Protocol would actually appear as emissions outsourced to developing countries. Like many other developed countries, the United States is a net importer of embodied greenhouse gas (GHG) emissions in trade. The goal of this study was to calculate the carbon footprint of various sectors of the U.S. economy using the latest available data and based on that, estimate the embodied carbon in certain manufacturing sector products imported and exported by the U.S. First, we conducted an input-output analysis to calculate the carbon footprint of 401 aggregate level sectors of the U.S. economy. After that, for a selected number of manufacturing subsectors /products(hereafter referred to as products),we collected their trade(import/export) data and combined that with their computed carbon footprint to estimate the embodied carbon in import and export of these products for the U.S. While we conducted the carbon footprint analysis for sectors covering the entire U.S. economy (401 sectors), we only conducted the embodied carbon in trade analysis for a few products within specific aggregate level manufacturing sectors,such as computer and electronic product manufacturing (NAICS 334), transport equipment manufacturing (NAICS 336), machinery manufacturing (NAICS 333), and chemical manufacturing (NAICS 325). It is possible to do the detailed embodied carbon analysis for all other products within the various aggregate level manufacturing sectors of the U.S. economy, but given the scope of this study and space constraint of this report, we only present the results for a few products. The results of our analysis for the selected products show the embodied carbon in trade for each product by country and also show the rankings of countries in terms of top importers and exporters of embodied carbon in trade with the U.S. Our results show that in majority of cases, the U.S. is a net importer of embodied carbon in trade. Unless consumption-based accounting is used, the U.S. and other net embodied carbon importing countries may continue to outsource their emissions to meet their climate change mitigation targets under the Paris Agreement, as observed previously with the Kyoto Protocol. Some countries reported reductions that exceeded their Kyoto targets, however, the changes in emissions embodied in imports were comparable to or larger than changes in domestic emissions. Traded emissions undermined emissions reductions in the Kyoto Protocol, and threaten to continue to do so for the Paris Agreement. New climate policies such as California’s Buy Clean act, EU’s proposed border carbon tax adjustment, as well as efforts in the private sector can help address the embodied carbon in traded products.

54 ENVIRONMENTAL SCIENCES↗

An operational definition of quark and gluon jets

While “quark” and “gluon” jets are often treated as separate, well-defined objects in both theoretical and experimental contexts, no precise, practical, and hadron-level definition of jet flavor presently exists. To remedy this issue, we develop and advocate for a data-driven, operational definition of quark and gluon jets that is readily applicable at colliders. Rather than specifying a per-jet flavor label, we aggregately define quark and gluon jets at the distribution level in terms of measured hadronic cross sections. Intuitively, quark and gluon jets emerge as the two maximally separable categories within two jet samples in data. Benefiting from recent work on data-driven classifiers and topic modeling for jets, we show that the practical tools needed to implement our definition already exist for experimental applications. As an informative example, we demonstrate the power of our operational definition using Z+jet and dijet samples, illustrating that pure quark and gluon distributions and fractions can be successfully extracted in a fully well-defined manner.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery

Advancing municipal solid waste (MSW) management from disposal-oriented practices toward circular, value-driven systems requires standardized methodologies capable of identifying material composition and resource recoverable potential at the point of generation. Despite extensive research, MSW characterization remains fragmented due to inconsistences in sampling methodologies, waste sorting categories, and temporal coverage across previous studies which limit cross-site comparability, reproducibility, and constrain the reliable evaluation of potential resource recovery pathways. This lack of consistency has hindered the development of a unified framework for MSW characterization and resource assessment. This study introduces a standardized, field-validated protocol for MSW sampling and composition analysis that ensures consistent, traceable data across diverse waste sources. The protocol integrates randomized spatial sampling, systematic material sorting, and controlled subsampling for multi-site and multi-season field campaigns. Validation included MSW collection from residential, grocery, restaurant, and school MSW streams across five U.S. states, including Maryland, Idaho, Virginia, Ohio, and Mississippi, to demonstrate the protocol’s ability to identify source-based composition patterns relevant to resource recovery applications. Grocery and restaurant streams were dominated by food waste and high-moisture organics, while school waste contained higher paper content and residential waste showed greater heterogeneity. Aggregation into energy-relevant fractions highlighted practical recovery pathways via anaerobic digestion or gasification, supporting data-driven planning, policy, and circular economy strategies for sustainable waste management across waste sources.

09 BIOMASS FUELS↗

Occupational Radiation Exposure Report for Calendar Year 2023

The U.S. Department of Energy Occupational Radiation Exposure Report for Calendar 2023 presents the results of analyses of occupational radiation exposures at the U.S. Department of Energy (DOE), including the National Nuclear Security Administration (NNSA) operations, during calendar year 2023. This report includes occupational radiation exposure data for over 80,000 DOE Federal employees, contractors, and subcontractors as well as members of the public who have worked in or entered controlled areas monitored for exposure to radiation. DOE publishes this annual report to provide DOE Management, Program Offices, workers, health physicists, and other stakeholders an evaluation of DOE-wide performance regarding compliance with Title 10 of the Code of Federal Regulations (CFR) Part 835, Occupational Radiation Protection (10 CFR 835) radiation exposure limits and adherence to as low as reasonably achievable principles. This report provides a discussion regarding radiation protection and exposure reporting requirements. It also includes calendar year (CY) 2023 information and analyses regarding aggregate, individual, site, DOE Program, transient individuals’ dose, as well as a historical review of DOE exposure data. DOE continues to be diligent in protecting its workers and the public from exposure to radiation from DOE operations as illustrated by the results contained in this report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Preferential Charge Generation at Aggregate Sites in Narrow Band Gap Infrared Photoresponsive Polymer Semiconductors

Infrared organic photodetector materials are investigated using transient absorption spectroscopy, demonstrating that ultrafast charge generation assisted by polymer aggregation is essential to compensate for the energy gap law, which dictates that excited state lifetimes decrease as the band gap narrows. Short sub–picosecond singlet exciton lifetimes are measured in a structurally related series of infrared–absorbing copolymers that consist of alternating cyclopentadithiophene electron–rich “push” units and strong electron–deficient “pull” units, including benzothiadiazole, benzoselenadiazole, pyridalselenadiazole, or thiadiazoloquinoxaline. While the ultrafast lifetimes of excitons localized on individual polymer chains suggest that charge carrier generation will be inefficient, high detectivity for polymer:PC 71 BM infrared photodetectors is measured in the 0.6 < λ < 1.5 µm range. The photophysical processes leading to charge generation are investigated by performing a global analysis on transient absorption data of blended polymer:PC 71 BM films. In these blends, charge carriers form primarily at polymer aggregate sites on the ultrafast time scale (within our instrument response), leaving quickly decaying single–chain excitons unquenched. Lastly, the results have important implications for the further development of organic infrared optoelectronic devices, where targeting processes such as excited state delocalization over aggregates may be necessary to mitigate losses to ultrafast exciton decay as materials with even lower band gaps are developed.

36 MATERIALS SCIENCE↗

Intelligent Vehicle Charging Benefits Assessment Using EV Project Data

PEVs can represent a significant power resource for the grid. An IVCI with bi-direction V2G capabilities would allow PEVs to provide grid support services and thus generate a source of revenue for PEV owners. The fleet of EV Project vehicles represents a power resource between 30 MW and 90 MW, depending on the power rating of the grid connection (5-15 kW). Aggregation of vehicle capacity would allow PEVs to participate in wholesale reserve capacity markets. One of the key insights from EV Project data is the fact that vehicles are connected to an EVSE much longer than is necessary to deliver a full charge. During these hours when the vehicles are not charging, they can be participating in wholesale power markets providing the high-value services of regulation and spinning reserves. The annual gross revenue potential for providing these services using the fleet of EV Project vehicles is several hundred thousands of dollars to several million dollars annually depending on the power rating of the grid interface, the number of hours providing grid services, and the market being served. On a per vehicle basis, providing grid services can generate several thousands of dollars over the life of the vehicle.

33 ADVANCED PROPULSION SYSTEMS↗

Data from: Learning coagulation processes with combinatorially-invariant neural networks

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

Atmospheric chemistry↗

Porosity and structural parameters of Karelian shungites according to the data of small-angle synchrotron radiation scattering and microscopy

The nanoporosity and structure of natural carbons has been investigated on the example of Karelian carbon-rich shungites by comparing the data of small-angle synchrotron radiation scattering and highresolution microscopy. The analysis of small-angle scattering data is based on the model of scattering spheres with lognormal size distribution. It is found that the structure of samples from the Maksovo and Zazhogino deposits subjected to high temperatures in the geological medium and (also to a lesser extent) a sample from the Shunga deposit can be described as an aggregation of polydisperse scattering spheres with lognormal size distribution; the characteristic scatterer size is determined for them. A comparison with microscopy data shows that these scatterers are mainly associated with pores, and the character of their size distribution is similar to that previously established for nanoglobules in schungites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

X-ray CT characterization and fracture simulation of ASR damage of glass particles in alkaline solution and mortar

The alkali-silica reaction (ASR) damage in reactive aggregates affects the long-term durability of the concrete infrastructure. The generated ASR gel can expand by imbibing water from the pore solution and the resulting expansion pressure causes the aggregate fracture. This study aims to simulate the development of ASR damage in glass particles of two types of samples (glass in alkali solution and glass mortars). The dynamic micro X-ray CT technique was conducted to monitor the crack propagation in glass aggregates at different reaction stages (up to 64 hrs). The Boundary Element Method (BEM) and Displacement Discontinuity Method (DDM) were used to efficiently simulate the crack propagation within irregular glass particles under gel expansion pressures. The aggregate boundaries were built with the BEM elements and the initial cracks were meshed with the DDM elements. The estimated expansion pressure was applied to the initial crack surfaces. The discontinuous displacements and stresses were calculated along the crack path and crack tip. The mixed-mode Stress Intensity Factors (SIFs) were calculated based on plane stress conditions. The maximum circumferential stress criteria was used to simulate the propagation of cracks along a specific angle. The simulation results include the simulated crack path and the combined SIFs changing with the increments. With the estimate expansion pressure, the glass particle damages were simulated within conditions of alkali solution and confined motar samples. The predicted crack propagation path was compared with the X-ray CT imaging data. The comparison results demonstrate the DDM has the ability to predicting the ASR damage propagation inside aggregates.

36 MATERIALS SCIENCE↗

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

54 ENVIRONMENTAL SCIENCES↗

Refining the aggregate exposure pathway

The elements of the aggregate exposure pathway have been refined to better facilitate organization, integration, and dissemination of exposure data.

Tan, Yu-Mei↗

Global vegetation model diversity and the risks of climate-driven ecosystem shifts

Climate change is modifying global biogeochemical cycles, and is expected to exert increasingly large effects in the future. How these changes will in turn affect and interact with the structure and function of particular ecosystems is unclear, however, both because of scientific uncertainties and the very diversity of global vegetation models in use. Writing in Environmental Research Letters, Warszawski et al. (1) aggregate results from a group of models, across a range of emissions scenarios and climate data, to investigate these risks. Although the models frequently disagree about which specific regions are at risk, they consistently predict a greater chance of ecosystem restructuring with more warming; this risk roughly doubles between 2 and 3 °C increases in global mean temperature. The innovative work of Warszawski et al. represents an important first step towards fully consistent multi-model, multi-scenario assessments of the future risks to global ecosystems.

climate change↗

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗