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DOE OSTI2024

Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution

Importance Opioid-related overdose accounts for almost 80 000 deaths annually across the US. People who use drugs leaving jails are at particularly high risk for opioid-related overdose and may benefit from take-home naloxone (THN) distribution. Objective To estimate the population impact of THN distribution at jail release to reverse opioid-related overdose among people with opioid use disorders. Design, Setting, and Participants This study developed the agent-based Justice-Community Circulation Model (JCCM) to model a synthetic population of individuals with and without a history of opioid use. Epidemiological data from 2014 to 2020 for Cook County, Illinois, were used to identify parameters pertinent to the synthetic population. Twenty-seven experimental scenarios were examined to capture diverse strategies of THN distribution and use. Sensitivity analysis was performed to identify critical mediating and moderating variables associated with population impact and a proxy metric for cost-effectiveness (ie, the direct costs of THN kits distributed per death averted). Data were analyzed between February 2022 and March 2024. Intervention Modeled interventions included 3 THN distribution channels: community facilities and practitioners; jail, at release; and social network or peers of persons released from jail. Main Outcomes and Measures The primary outcome was the percentage of opioid-related overdose deaths averted with THN in the modeled population relative to a baseline scenario with no intervention. Results Take-home naloxone distribution at jail release had the highest median (IQR) percentage of averted deaths at 11.70% (6.57%-15.75%). The probability of bystander presence at an opioid overdose showed the greatest proportional contribution (27.15%) to the variance in deaths averted in persons released from jail. The estimated costs of distributed THN kits were less than $\$$15 000 per averted death in all 27 scenarios. Conclusions and Relevance This study found that THN distribution at jail release is an economical and feasible approach to substantially reducing opioid-related overdose mortality. Training and preparation of proficient and willing bystanders are central factors in reaching the full potential of this intervention.

Tatara, Eric [Argonne National Laboratory (ANL), A↗

DOE OSTI2022

Revealing the Electrophilic‐Attack Doping Mechanism for Efficient and Universal p‐Doping of Organic Semiconductors

Abstract Doping is of great importance to tailor the electrical properties of semiconductors. However, the present doping methodologies for organic semiconductors (OSCs) are either inefficient or can only apply to some OSCs conditionally, seriously limiting their general applications. Herein, a novel p‐doping mechanism is revealed by investigating the interactions between the dopant trityl tetrakis(pentafluorophenyl) borate (TrTPFB) and poly(3‐hexylthiophene) (P3HT). It is found that electrophilic attack of the trityl cations on thiophenes results in the formation of tritylated thiophenium ions, which subsequently induce electron transfer from neighboring P3HT chains to realize p‐doping. This unique p‐doping mechanism enables TrTPFB to p‐dope various OSCs including those with high ionization energy (IE ≈ 5.8 eV). Moreover, this doping mechanism endows TrTPFB with strong doping capability, leading to doping efficiency of over 80% in P3HT. The discovery and elucidation of this novel doping mechanism not only points out that strong electrophiles are a class of efficient p‐dopants for OSCs, but also provides new opportunities toward highly efficient doping of various OSCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

The packing problem of uncertain multicasts

Summary Multicast performs better than unicast in delivering the same content from a fixed single source to a set of destinations. Many efforts have been made to optimize such kind of deterministic multicast, such as minimizing the transmission cost of each multicast session. In practice, it is not necessary that the source of each multicast session has to be in a specific location, as long as certain constraints are satisfied. Accordingly, applications usually meet a novel multicast with uncertain sources, ie, uncertain multicast. That is, multiple nodes have the responsibility to act as the root node of a multicast session. Prior proposals have addressed an uncertain multicast by constructing the minimum cost forest. However, it is still unknown how to efficiently share the network resources, when a set of uncertain multicast occupies the network simultaneously. To tackle such a challenging issue, we present the packing problem of uncertain multicasts (MPU) to minimize the total transmission cost, under the constraint of link capacity. We prove that the MPU problem is NP‐hard. An intrinsic solution is constructing the minimum cost forest for each uncertain multicast individually. This method, however, is inefficient and may be infeasible because of the constraint of link capacity. Thus, we design 2 dedicated greedy methods, named priority‐based and adjusting congested link, to approximate the optimal solution. The comprehensive results indicate that both of our 2 methods can find a feasible solution for the MPU problem. Moreover, given a set of uncertain multicasts, the adjusting congested link method can generate a desired transmission structure for each uncertain multicast and achieve the least total cost when packing them.

Ren, Bangbang↗

DOE OSTI2018

TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML

Summary Deep learning has proven to be a successful tool for solving a large variety of problems in various scientific fields and beyond. In recent years, the models as well as the available datasets have grown bigger and more complicated, and thus, an increasing amount of computing resources is required in order to train these models in a reasonable amount of time. Besides being able to use HPC resources, deep learning model developers want flexible frameworks which allow for rapid prototyping. One of the most important of these frameworks is Google TensorFlow, which provides both features, ie, good performance as well as flexibility. In this paper, we discuss different solutions for scaling the TensorFlow Framework to thousands of nodes on contemporary Cray XC supercomputing systems.

Kurth, Thorsten↗

DOE OSTI2018

Contributions of Material Properties and Structure to Increased Bone Fragility for a Given Bone Mass in the UCD-T2DM Rat Model of Type 2 Diabetes

Adults with type 2 diabetes (T2D) have a higher fracture risk for a given bone quantity, but the mechanisms remain unclear. Using a rat model of polygenic obese T2D, we demonstrate that diabetes significantly reduces whole-bone strength for a given bone mass (μCT-derived BMC), and we quantify the roles of T2D-induced deficits in material properties versus bone structure; ie, geometry and microarchitecture. Lumbar vertebrae and ulnae were harvested from 6-month-old lean Sprague-Dawley rats, obese Sprague-Dawley rats, and diabetic obese UCD-T2DM rats (diabetic for 69 ± 7 days; blood glucose >200 mg/dL). Both obese rats and those with diabetes had reduced whole-bone strength for a given BMC. In obese rats, this was attributable to structural deficits, whereas in UCD-T2DM rats, this was attributable to structural deficits and to deficits in tissue material properties. For the vertebra, deficits in bone structure included thinner and more rod-like trabeculae; for the ulnae, these deficits included inefficient distribution of bone mass to resist bending. Deficits in ulnar material properties in UCD-T2DM rats were associated with increased non-enzymatic crosslinking and impaired collagen fibril deformation. Specifically, small-angle X-ray scattering revealed that diabetes reduced collagen fibril ultimate strain by 40%, and those changes coincided with significant reductions in the elastic, yield, and ultimate tensile properties of the bone tissue. Importantly, the biomechanical effects of these material property deficits were substantial. Prescribing diabetes-specific tissue yield strains in high-resolution finite element models reduced whole-bone strength by a similar amount (and in some cases a 3.4-fold greater amount) as the structural deficits. These findings provide insight into factors that increase bone fragility for a given bone mass in T2D; not only does diabetes associate with less biomechanically efficient bone structure, but diabetes also reduces tissue ductility by limiting collagen fibril deformation, and in doing so, reduces the maximum load capacity of the bone.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Expression of a Degradation-Resistant β-Catenin Mutant in Osteocytes Protects the Skeleton From Mechanodeprivation-Induced Bone Wasting

ABSTRACT Mechanical stimulation is a key regulator of bone mass, maintenance, and turnover. Wnt signaling is a key regulator of mechanotransduction in bone, but the role of β-catenin—an intracellular signaling node in the canonical Wnt pathway—in disuse mechanotransduction is not defined. Using the β-catenin exon 3 flox (constitutively active [CA]) mouse model, in conjunction with a tamoxifen-inducible, osteocyte-selective Cre driver, we evaluated the effects of degradation-resistant β-catenin on bone properties during disuse. We hypothesized that if β-catenin plays an important role in Wnt-mediated osteoprotection, then artificial stabilization of β-catenin in osteocytes would protect the limbs from disuse-induced bone wasting. Two disuse models were tested: tail suspension, which models fluid shift, and botulinum-toxin (botox)-induced muscle paralysis, which models loss of muscle force. Tail suspension was associated with a significant loss of tibial bone mass and density, reduced architectural properties, and decreased bone formation indices in uninduced (control) mice, as assessed by dual-energy X-ray absorptiometry (DXA), micro-computed tomography (µCT), and histomorphometry. Activation of the βcatCA allele in tail-suspended mice resulted in little to no change in those properties; ie, these mice were protected from bone loss. Similar protective effects were observed among botox-treated mice when the βcatCA was activated. RNAseq analysis of altered gene regulation in tail-suspended mice yielded 35 genes, including Wnt11, Gli1, Nell1, Gdf5, and Pgf, which were significantly differentially regulated between tail-suspended β-catenin stabilized mice and tail-suspended nonstabilized mice. Our findings indicate that selectively targeting/blocking of β-catenin degradation in bone cells could have therapeutic implications in mechanically induced bone disease. © 2019 American Society for Bone and Mineral Research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Simulation of discrete cracks driven by nearly incompressible fluid via 2D combined finite-discrete element method

Here, we propose a novel hydraulic solver in order to simulate key mechanisms that control fluid-driven cracks in the framework of the combined finite-discrete element method (FDEM). The main innovative aspect of the present work is the independence of the fluid's critical time step size with the fracture opening. This advantage is extremely important because it means that very fine meshes can be used around areas of interest, such as boreholes, without penalizing the computational cost as fractures propagate (ie, open) and the fluid flows through them. This is a great advantage over other recently introduced approaches that exhibit a dependency of the time step in the form of Δt crit ∝ (l/a) 2 where l is the element size and a is the fracture aperture. This paper presents a series of benchmark cases for the proposed solver. The rationale adopted by the authors was to benchmark and validate the implementation of the hydraulic solver in an incremental fashion, starting from the simplest cases and building in complexity. The calculations shown in this work clearly demonstrate that the proposed approach is able to reproduce analytical results for fluid flow through a single crack. The results presented in this paper also demonstrate that the new approach is robust enough to deal with complex fracture patterns and complex geometries; the obtained fluid-driven fracture patterns in the vicinity of a borehole certainly stand to the scrutiny of human visual perception.

58 GEOSCIENCES↗

DOE OSTI2017

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Generalized radial-return mapping algorithm for anisotropic von Mises plasticity framed in material eigenspace

A computationally efficient integration algorithm for anisotropic plasticity is proposed, which is identified as a generalization of the radial-return mapping algorithm to anisotropy. The algorithm is based upon formulation within the eigenspace of a material anisotropy tensor associated with anisotropic quadratic von Mises (J 2 ) plasticity (also called Hill plasticity), for which it is shown to ensure that the flow rule remains associative, ie, the normality condition is satisfied. Extension of the algorithm to include anisotropic elasticity (anisotropic elastoplasticity) is further provided, made possible by the identification of a certain fourth-order material tensor dependent on both the elastic and plastic anisotropy. The derivation of the fully elastoplastically anisotropic algorithm involves further complexity, but the resulting algorithm is shown to closely resemble the purely plastically anisotropic one once the appropriate eigenspace is identified. The proposed generalized radial-return algorithm is compared to a classical closest-point projection algorithm, for which it is shown to provide considerable advantage in computational cost. In conclusion, the efficiency, accuracy, and robustness of the algorithm are demonstrated through various illustrative test cases and in the finite element simulation of Taylor impact tests on tantalum.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Degradation in photovoltaic encapsulation strength of attachment: Results of the first PVQAT TG5 artificial weathering study

Delamination of the encapsulant in photovoltaic (PV) module technology results in immediate optical loss and may enable subsequent corrosion or mechanical damage. The effects of artificial weathering were not previously known; therefore, an empirical study was performed to survey the factors most affecting adhesion, including the ultraviolet (UV) source (ie, Xe or fluorescent lamp[s]), the optical filters for the lamp, the chamber temperature, and the relative humidity. Natural weathering was also performed at locations, including the following: Golden, Colorado; Miami, Florida; Phoenix, Arizona; QiongHai, China; Riyadh, Saudi Arabia; and Turpan, China. Specimens were constructed using a laminated glass/poly (ethylene-co-vinyl acetate)/glass geometry. The compressive shear test (CST) was used to quantify the mechanical strength of attachment, taken as a proxy for adhesion. The fractography of select specimens (including cross-sectional optical microscopy) was used to verify the failure mode (delamination or decohesion). Additional analysis of the local solar spectrum as well as the specimen temperature was performed to interpret the results of natural weathering. The goals of this study include to identify the most significant stressors, clarify where strong coupling may occur between stressors, and validate accelerated test results relative to natural weathering. The importance of specimen conditioning (for moisture) and hygrometric degradation as well as the effects of UV degradation are identified in this paper.

14 SOLAR ENERGY↗

DOE OSTI2020

Many‐body response of benzene at monolayer MoS 2 : Van der Waals interactions and spectral broadening

Abstract Models of surface enhancement of molecular electronic response properties are challenging for two reasons: (a) molecule‐surface interactions require a simultaneous solution of the molecular and the surface dynamic response (a daunting task), and (b) when solving for the electronic structure of the combined molecule + surface system, it is not trivial to single out the particular physical effects responsible for enhancement. To tackle this problem, in this work, we apply a formally exact decomposition of the system's response function into subsystem contributions by using subsystem density functional theory (DFT), which grants access to dynamic polarizabilities and optical spectra. In order to access information about the interactions between the subsystems, we extend a previously developed subsystem‐based adiabatic connection fluctuation‐dissipation theorem of DFT to separate the additive from the nonadditive correlation energy and identify the nonadditive correlation as the van der Waals interactions. As an example, we choose benzene adsorbed on monolayer MoS 2 . We isolate the contributions to benzene's dynamic response arising from the interaction with the surface, and for the first time, we evaluate the enhancements to the effectiveness of C 6 coefficients as a function of benzene‐MoS 2 distance and adsorption site. We also quantify the spectral broadening of the benzene's electronic excited states due to their interaction with the surface. We find that the broadening has a similar decay law with the molecule‐surface distance as the leading van der Waals interactions (ie, R −6 ) and that the surface enhancement of dispersion interactions between benzene molecules is less than 5% but is still large enough (0.5 kcal/mol) to likely play a role in the prediction of interface morphologies.

Umerbekova, Alina↗

DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗

DOE OSTI2023

Transferable Adversarial Attack on 3D Object Tracking in Point Cloud

3D point cloud object tracking has recently witnessed considerable progress relying on deep learning. Such progress, however, mainly focuses on improving tracking accuracy. The risk, especially considering that deep neural network is vulnerable to adversarial perturbations, of a tracker being attacked is often neglected and rarely explored. In order to attract attentions to this potential risk and facilitate the study of robustness in point cloud tracking, we introduce a novel transferable attack network (TAN) to deceive 3D point cloud tracking. Specifically, TAN consists of a 3D adversarial generator, which is trained with a carefully designed multi-fold drift (MFD) loss. The MFD loss considers three common grounds, including classification, intermediate feature and angle drifts, across different 3D point cloud tracking frameworks for perturbation generation, leading to high transferability of TAN for attack. In our extensive experiments, we demonstrate the proposed TAN is able to not only drastically degrade the victim 3D point cloud tracker, \ie, P2B, but also effectively deceive other unseen state-of-the-art approaches such as BAT and M^2Track, posing a new threat to 3D point cloud tracking.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dark photon dark matter in the presence of inhomogeneous structure

Dark photon dark matter will resonantly convert into visible photons when the dark photon mass is equal to the plasma frequency of the ambient medium. In cosmological contexts, this transition leads to an extremely efficient, albeit short-lived, heating of the surrounding gas. Existing work in this field has been predominantly focused on understanding the implications of these resonant transitions in the limit that the plasma frequency of the Universe can be treated as being perfectly homogeneous, \ie neglecting inhomogeneities in the electron number density. In this work we focus on the implications of heating from dark photon dark matter in the presence of inhomogeneous structure (which is particularly relevant for dark photons with masses in the range $10^{-15} \, {\rm eV} \, \lesssim m_{A^\prime} \lesssim 10^{-12}$ eV), emphasizing both the importance of inhomogeneous energy injection, as well as the sensitivity of cosmological observations to the inhomogeneities themselves. More specifically, we derive modified constraints on dark photon dark matter from the Ly-$\alpha$ forest, and show that the presence of inhomogeneities allows one to extend constraints to masses outside of the range that would be obtainable in the homogeneous limit, while only slightly relaxing their strength. We then project sensitivity for near-future cosmological surveys that are hoping to measure the 21cm transition in neutral hydrogen prior to reionization, and demonstrate that these experiments will be extremely useful in improving sensitivity to masses near $\sim 10^{-14}$ eV, potentially by several orders of magnitude. Finally, we discuss implications for reionization, early star formation, and late-time $y$-type spectral distortions, and show that probes which are inherently sensitive to the inhomogeneous state of the Universe could resolve signatures unique to the light dark photon dark matter scenario, and thus offer a fantastic potential for a positive detection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

An Inverse Factorial Series for a General Gamma Ratio and Related Properties of the Nørlund–Bernoulli Polynomials

The inverse factorial series expansion for the ratio of products of gamma functions whose arguments are linear functions of the variable is found. A recurrence relation for the coefficients in terms of the Nørlund–Bernoulli polynomials is provided, and the half-plane of convergence is determined. The results obtained naturally supplement a number of previous investigations of the gamma ratios, which began in the 1930-ies. The expansion obtained in this paper plays a crucial role in the study of the behavior of the delta-neutral Fox’s H-function in the neighborhood of its finite singular point. A particular case of the inverse factorial series expansion is used in deriving a possibly new identity for the Nørlund–Bernoulli polynomials.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Effects of copper loading on NH3-SCR and NO oxidation over Cu impregnated CHA zeolite

Cu/CHA catalysts with various Cu loadings (0.5 wt%–6.0 wt%) were synthesized via incipient wetness impregnation. The catalysts were then applied to the selective catalytic reduction (SCR) of NO with NH 3 and NO oxidation reaction. XRD and N 2 adsorption-desorption data showed that CHA structure was maintained with the incorporation of Cu, while specific surface areas decreased with increasing Cu loading. At intermediate Cu loading, 4 wt%, the highest NH 3 -SCR activity was observed with ~98% N2 selectivity from 150°C to 300°C. Small amounts of water, 2%, slightly increased NO conversion in addition to the remarkable N 2 O and NO 2 reduction at high temperature. Water effects are attributed to the improved Cu ion reducibility and mobility. NO oxidation results provided no relation between NO 2 formation and SCR activity. Physicochemical properties, NO conversion, N 2 selectivity, and activation energy data showed that impregnated samples’ molecular structure and catalytic activity are comparable to the conventional ion-exchanged (IE) samples’ ones.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Role of {sup 18}F-FDG PET/CT in the diagnosis of cardiovascular implantable electronic device infections: A meta-analysis

Objective: We performed a meta-analysis evaluating the use of fluorine-18-fluorodeoxyglucose ({sup 18}F-FDG) positron-emission tomography (PET)/computed tomography (CT) in the diagnosis of cardiovascular implantable electronic device (CIED) infections. Background: PET/CT may be helpful in the diagnosis of CIED infection, particularly in patients with the absence of localizing signs or definitive echocardiographic findings. Methods: PubMed, Embase, Cochrane library, CINAHL, Web of Knowledge, and www.clinicaltrials.gov from January 1990 to April 2017 were searched for studies evaluating the accuracy of PET/CT in the diagnosis of CIED infections. Results: Overall, 14 studies involving 492 patients were included in the meta-analysis. The pooled sensitivity of PET/CT for diagnosis of CIED infection was 83% (95% CI 78%-86%) and the pooled specificity was 89% (95% CI 84%-94%). PET/CT demonstrated a higher sensitivity of 96% (95% CI 86%-99%) and specificity of 97% (95% CI 86%-99%) for diagnosis of pocket infections. Diagnostic accuracy for lead infections or CIED-IE was lower with pooled sensitivity of 76% (95% CI 65%-85%) and specificity of 83% (95% CI 72%-90%). Conclusion: Use of PET/CT in the evaluation of CIED infection has both a high sensitivity (83%) and specificity (89%) and deserves consideration in the management of selected patients with suspected CIED infections.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Estimating Energy Market Schedules using Historical Price Data

The global climate crisis is expected to reshape the energy generation landscape in the coming decades. Increasing integration of non-dispatchable renewable energy resources into energy infrastructures and markets creates uncertainty as well as new opportunities for flexible energy systems. To conduct proper economic evaluation of flexible energy systems, such as integrated energy systems (IES), advancements in modelling of market interactions, such as bidding, is crucial. This work presents a shortcut algorithm which uses two mixed integer linear programs to compute dispatch schedules (e.g., hourly power production targets) that are constrained by the resource's bid information and characteristics (e.g., minimum up and down times) based on historical locational marginal price (LMP) data. The proposed algorithm is approximately 100 times faster and uses orders of magnitude less data than a full production cost model (PCM). We find the shortcut simulator recapitulates generator dispatch signals for the Prescient PCM with approximately 4% error for the RTS-GMLC test system.

electricity generation↗