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At least 1,063 records · Page 59

Dominant Energy Carrier Transitions and Thermal Anisotropy in Epitaxial Iridium Thin Films

High aspect ratio metal nanostructures are commonly found in a broad range of applications such as electronic compute structures and sensing. The self-heating and elevated temperatures in these structures, however, pose a significant bottleneck to both the reliability and clock frequencies of modern electronic devices. Any notable progress in energy efficiency and speed requires fundamental and tunable thermal transport mechanisms in nanostructured metals. Here, in this work, time-domain thermoreflectance is used to expose cross-plane quasi-ballistic transport in epitaxially grown metallic Ir(001) interposed between Al and MgO(001). Thermal conductivities ranges from roughly 65 (96 in-plane) to 119 (122 in-plane) W m -1 K -1 for 25.5–133.0 nm films, respectively. Further, low defects afforded by epitaxial growth are suspected to allow the observation of electron–phonon coupling effects in sub-20 nm metals with traditionally electron-mediated thermal transport. Via combined electro-thermal measurements and phenomenological modeling, the transition is revealed between three modes of cross-plane heat conduction across different thicknesses and an interplay among them: electron dominant, phonon dominant, and electron–phonon energy conversion dominant. The results substantiate unexplored modes of heat transport in nanostructured metals, the insights of which can be used to develop electro-thermal solutions for a host of modern microelectronic devices and sensing structures.

36 MATERIALS SCIENCE↗

High Velocity, Low‐Voltage Collective In‐Plane Switching in (100) BaTiO 3 Thin Films

Abstract Ferroelectrics are being increasingly called upon for electronic devices in extreme environments. Device performance and energy efficiency is highly correlated to clock frequency, operational voltage, and resistive loss. To increase performance it is common to engineer ferroelectric domain structure with highly‐correlated electrical and elastic coupling that elicit fast and efficient collective switching. Designing domain structures with advantageous properties is difficult because the mechanisms involved in collective switching are poorly understood and difficult to investigate. Collective switching is a hierarchical process where the nano‐ and mesoscale responses control the macroscopic properties. Using chemical solution synthesis, epitaxially nearly‐relaxed (100) BaTiO 3 films are synthesized. Thermal strain induces a strongly‐correlated domain structure with alternating domains of polarization along the [010] and [001] in‐plane axes and 90° domain walls along the [011] or [01] directions. Simultaneous capacitance–voltage measurements and band‐excitation piezoresponse force microscopy revealed strong collective switching behavior. Using a deep convolutional autoencoder, hierarchical switching is automatically tracked and the switching pathway is identified. The collective switching velocities are calculated to be ≈500 cm s −1 at 5 V (7 kV cm −1 ), orders‐of‐magnitude faster than expected. These combinations of properties are promising for high‐speed tunable dielectrics and low‐voltage ferroelectric memories and logic.

36 MATERIALS SCIENCE↗

Speedup of UEDGE Parameter Scans Using Machine-Learning Optimized OpenMP Parallelization and a Continuation Solver

This article presents the OpenMP parallelization of the preconditioning Jacobian assembly and right‐hand side residual evaluation in UEDGE. A continuation algorithm, utilizing the internal NKSOL implicit Jacobian‐Free Newton‐Krylov solver to efficiently scan physical parameters, is also presented. The implemented parallelization reduces the computational time for a benchmark scan run on 32 threads by compared to the serial version when using trained random forest regression models to identify the optimal decomposition of the system of equations. Random forest regression models applied to the UEDGE time‐dependent and continuation solver algorithms did not yield meaningful improvement in computational performance. A benchmark DIII‐D gas injection rate scan in the 0.35–0.75 kA interval, performed on a test cluster using the parallelized code and continuation solver, produced 1066 steady‐state solutions with a 22 s average wall‐clock computational time per steady‐state solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Regional Earthquake Ground Motion Simulations for Southern California With EQSIM: Insights From the 2008 Chino Hills, 2024 Highland Park, and 2021 Carson Earthquakes

This study presents physics-based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics-based ground motion simulations to reproduce the observed motions from historical events. The selected events included the mathematical equation M w 5.4 2008 Chino Hills, the mathematical equation M w 4.4 2024 Highland Park, and the mathematical equation M w 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM-S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near-source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics-based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall-clock times of simulations involving more than 10 billion grid points were as low as mathematical equation minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.

EQSIM↗

Circadian rhythmicity of amyloid‐beta‐related molecules is disrupted in the choroid plexus of a female Alzheimer's disease mouse model

Abstract The choroid plexus (CP) is part of the blood‐cerebrospinal fluid barrier (BCSFB) and was recently described as an important component of the circadian clock system. It is the principal source of cerebrospinal fluid (CSF) and responsible for the synthesis and secretion of various neuroprotective peptides including those involved in amyloid‐β (Aβ) transport/degradation, contributing to Aβ homeostasis. Inadequate Aβ metabolic clearance and transport across the BCSFB have been associated with circadian dysfunctions in Alzheimer's disease (AD) patients. To investigate whether AD pathology influences Aβ scavengers circadian expression, we collected CP at different time points from an AD mouse model (APP/PS1) (female and male animals, aged 6‐ and 12‐months‐old) and analyzed their mRNA expression by Real‐time RT‐PCR. Only angiotensin‐converting enzyme (Ace) expression in 6‐month‐old female wild‐type mice and transthyretin (Ttr) expression in 12‐month‐old female wild‐type mice presented significant rhythmicity. The circadian rhythmicity of Ace and Ttr, prompt us to analyze the involvement of circadian rhythm in Aβ uptake. A human CP papilloma (HIBCPP) cell line was incubated with Aβ‐488 and uptake was evaluated at different time points using flow cytometry. Aβ uptake displayed circadian rhythmicity. Our results suggest that AD might affect Aβ scavengers rhythmicity and that Aβ clearance is a rhythmic process possibly regulated by the rhythmic expression of Aβ scavengers.

Furtado, André↗

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗

Commercialization of microcavity plasma devices and arrays: Systems for VUV photolithography and nanopatterning, disinfection of drinking water and air, and biofilm deactivation for medical therapeutics

Abstract A little more than two decades after the introduction of the first microcavity plasma devices, a growing body of commercial products based on the remarkable properties of these low‐temperature, atmospheric plasmas is now available. Following a brief review of early microplasma lamp development, this article describes microplasma‐based devices and systems currently being manufactured for applications in photolithography, photopatterning, and other nanofabrication processes, such as atomic layer deposition, spectroscopic identification and analysis of materials (including wide bandgap crystals), the disinfection of drinking water in off‐grid communities, deactivating COVID‐19 and other pathogens in air and on surfaces in public spaces, and vacuum ultraviolet lamps for driving atomic clocks. The microplasma‐based treatment of otitis media in the human ear will also be discussed.

42 ENGINEERING↗

Cytochrome P450s CYP380C6 and CYP380C9 in green peach aphid facilitate its adaptation to indole glucosinolate‐mediated plant defense

Abstract BACKGROUND Overexpressing CIRCADIAN CLOCK ASSOCIATED1 in Arabidopsis thaliana ( CCA1‐ox ) increases indole glucosinolate production and resistance to green peach aphid ( Myzus persicae ). Little is known of how aphids respond to this group of plant defense compounds or of the underlying molecular mechanism. Results Aphids reared on CCA1‐ox for over 40 generations (namely the CCA population) became less susceptible to CCA1‐ox than aphids maintained on the wild‐type Col‐0 (namely the COL population). This elevated tolerance was transgenerational as it remained for at least eight generations after the CCA population was transferred to Col‐0. Intriguingly, transcriptome analysis indicated that all differential cytochrome P450 monooxygenase genes ( MpCYPs ), primarily MpCYP4 s, MpCYP380s and MpCYP6 s, were more highly expressed in the CCA population. Application of a P450 inhibitor to the CCA population resulted in decreased aphid reproduction on CCA1‐ox , which was not observed if aphids were reared on Col‐0. When indole glucosinolate biosynthesis in CCA1‐ox was blocked using virus‐induced gene silencing, the effect of the P450 inhibitor on the CCA population was attenuated, affirming the essential role played by MpCYPs in counteracting the defense mechanism in CCA1‐ox that is low or absent in Col‐0. Furthermore, we used host‐induced gene silencing to identify MpCYP380C6 and MpCYP380C9 that specifically facilitated the CCA population to cope with CCA1 ‐mediated plant defense. Expression profiles revealed their possible contribution to the transgenerational tolerance observed in aphids. Conclusion MpCYP380C6 and MpCYP380C9 in aphids play a crucial role in mitigating indole glucosinolate‐mediated plant defense, and this effect is transgenerational.

Ji, Rui↗

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗

Null Raychaudhuri: canonical structure and the dressing time

Abstract We initiate a study of gravity focusing on generic null hypersurfaces, non-perturbatively in the Newton coupling. We present an off-shell account of the extended phase space of the theory, which includes the expected spin-2 data as well as spin-0, spin-1 and arbitrary matter degrees of freedom. We construct the charges and the corresponding kinematic Poisson brackets, employing a Beltrami parameterization of the spin-2 modes. We explicitly show that the constraint algebra closes, the details of which depend on the non-perturbative mixing between spin-0 and spin-2 modes. Finally we show that the spin zero sector encodes a notion of a clock, called dressing time, which is dynamical and conjugate to the constraint. It is well-known that the null Raychaudhuri equation describes how the geometric data of a null hypersurface evolve in null time in response to gravitational radiation and external matter. Our analysis leads to three complementary viewpoints on this equation. First, it can be understood as a Carrollian stress tensor conservation equation. Second, we construct spin-0, spin-2 and matter stress tensors that act as generators of null time reparametrizations for each sector. This leads to the perspective that the null Raychaudhuri equation can be understood as imposing that the sum of CFT-like stress tensors vanishes. Third, we solve the Raychaudhuri constraint non-perturbatively. The solution relates the dressing time to the spin-2 and matter boost charge operators. Finally we establish that the corner charge corresponding to the boost operator in the dressing time frame is monotonic. These results show that the notion of an observer can be thought of as emerging from the gravitational degrees of freedom themselves. We briefly mention that the construction offers new insights into focusing conjectures.

Physics↗

Search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb -1 of $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for physics beyond the Standard Model inducing periodic signals in the dielectron and diphoton invariant mass spectra is presented using 139 fb -1 of $\sqrt{s}$ = 13 TeV pp collision data collected by the ATLAS experiment at the LHC. Novel search techniques based on continuous wavelet transforms are used to infer the frequency of periodic signals from the invariant mass spectra and neural network classifiers are used to enhance the sensitivity to periodic resonances. In the absence of a signal, exclusion limits are placed at the 95% confidence level in the two-dimensional parameter space of the clock work gravity model. Model-independent searches for deviations from the background-only hypothesis are also performed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

New inflationary probes of axion dark matter

Abstract If a light axion is present during inflation and becomes part of dark matter afterwards, its quantum fluctuations contribute to dark matter isocurvature. In this article, we introduce a whole new suite of cosmological observables for axion isocurvature, which could help test the presence of axions, as well as its coupling to the inflaton and other heavy spectator fields during inflation such as the radial mode of the Peccei-Quinn field. They include correlated clock signals in the curvature and isocurvature spectra, and mixed cosmological-collider non-Gaussianities involving both curvature and isocurvature fluctuations with shapes and running unconstrained by the current data analyses. Taking into account of the existing strong constraints on axion isocurvature fluctuations from the CMB, these novel signals could still be sizable and potentially observable. In some models, the signals, if observed, could even help us significantly narrow down the range of the inflationary Hubble scale, a crucial parameter difficult to be determined in general, independent of the tensor mode.

Physics↗

The SEEL motif and members of the MYB-related REVEILLE transcription factor family are important for the expression of LORELEI in the synergid cells of the Arabidopsis female gametophyte

Synergid cells in the micropylar end of the female gametophyte are required for critical cell–cell signaling interactions between the pollen tube and the ovule that precede double fertilization and seed formation in flowering plants. LORELEI (LRE) encodes a putative GPI-anchored protein that is expressed primarily in the synergid cells, and together with FERONIA, a receptor-like kinase, it controls pollen tube reception by the receptive synergid cell. Still, how LRE expression is controlled in synergid cells remains poorly characterized. We identified candidate cis-regulatory elements enriched in LRE and other synergid cell-expressed genes. One of the candidate motifs (‘TAATATCT’) in the LRE promoter was an uncharacterized variant of the Evening Element motif that we named as the Short Evening Element-like (SEEL) motif. Deletion or point mutations in the SEEL motif of the LRE promoter resulted in decreased reporter expression in synergid cells, demonstrating that the SEEL motif is important for expression of LRE in synergid cells. Additionally, we found that LRE expression is decreased in the loss of function mutants of REVEILLE (RVE) transcription factors, which are clock genes known to bind the SEEL and other closely related motifs. Here, we propose that RVE transcription factors regulate LRE expression in synergid cells by binding to the SEEL motif in the LRE promoter. Identification of cis-regulatory elements and transcription factors involved in the expression of LRE will serve as a foundation to characterize the gene regulatory networks in synergid cells.

59 BASIC BIOLOGICAL SCIENCES↗

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.

97 MATHEMATICS AND COMPUTING↗

An assessment of the utility of multirate time integration for the modeling of laser powder bed fusion

Finite element simulation of the laser powder bed fusion process is made challenging by the inherently multiscale nature of the process. When using the typical global time stepping techniques, slowly-evolving regions of the domain receive the same numerical treatment as the regions with the highest temperature rates. The current work details the implementation and evaluation of an implicit multirate method which is able to advance different regions of the domain with distinct time steps, depending on their current solution rate. Previous work indicates that this representation of the temporal scales of the problem can lead to significant reductions in wall clock run time, and it is shown herein that multirate time integration, when used with a uniform process-scale mesh, can result in speedups of approximately 19, 42, and 87 times, for domains with edge lengths of approximately 1 mm, 2 mm, and 5 mm, respectively. When used in conjunction with $\ h$-refinement (limited to two levels of refinement), the resulting speedups (taken relative to the uniform mesh) are around 28, 85, and 665 times. The method is demonstrated to converge as indicated by the literature, and its use with an AM-Bench domain is demonstrated.

42 ENGINEERING↗

Data reduction in deterministic neutron transport calculations using machine learning

Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. Here, this information can be a limiting factor when using the multigroup discrete ordinates (S N ) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Concerning Mössbauer experiments in a rotating system and their physical interpretation

We shortly review different attempts to interpret the results of Mössbauer rotor experiments in a rotating system and particularly we show that the latest work on this subject by J. Iovane and E. Benedetto (2019), which claims that the outcomes of these experiments can supposedly be explained via “desynchronization of clocks” in the rotating frame and in the laboratory frame, is inapplicable to all of the Mössbauer rotor experiments performed up to date and thus does not have any significance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗