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Inverse design of membrane-type acoustic metamaterial based on deep learning
Membrane acoustic metamaterial (MAM) design mainly relies on forward design methods, making it difficult to determine structural parameters directly from predefined acoustic requirements. In this study, an inverse design framework for rubber-metal composite mass-block MAM (RMMAM) is proposed. A LiveLink-based COMSOL–MATLAB simulation interface is established to automatically generate finite element datasets, and a dual attention convolutional neural network based on the EfficientNet-feature pyramid architecture is trained to predict structural parameters from target sound transmission loss (STL) curves. Unlike conventional forward strategies, the proposed framework directly maps target STL curves to the corresponding RMMAM parameters. With target STL curves as input, the prediction error of structural parameters is controlled within 10.5%. In addition, STL curves reconstructed from the predicted parameters exhibit peak-frequency errors of 5–20 Hz and STL errors of 1.13–10.92 dB, demonstrating the reliability of the inverse design model. Case studies are further conducted under the constraint that the peak sound insulation level should not be lower than 50 dB in the 300–400 Hz frequency band. Both numerical simulations and experiments confirm that the designed RMMAM structures satisfy the predefined sound insulation requirements. The proposed framework provides an efficient route for the inverse design of acoustic metamaterials and supports their engineering applications.
Active linking through narratives facilitates associative inference
In daily life, we often draw inferences about novel associations from prior experiences. This ability, associative inference, is thought to be a key process by which memory guides future behavior. Prior studies have typically instructed participants to use narratives or mental imagery to aid memory of object associations. However, these studies have not explicitly tested the role of narratives, leaving an important question: how crucial are narratives in associative inference, and what role might they play? We used a modified associative inference paradigm to answer these questions. Our design factorially combined two factors during associative encoding: use of narratives, and explicitly linking the two objects. Participants separately learned AB and BC object pairs within ABC triads using four sets of instructions: i) generate narratives to link objects together, ii) generate narratives for objects separately, iii) compare perceptual properties of two objects, and iv) describe perceptual properties of objects individually. We tested participants’ ability to infer AC associations as a function of encoding strategy. Across two experiments, using narratives to actively link objects produced a robust associative memory advantage that persisted across 1-day delay and remained substantial under more stringent retrieval conditions. This benefit was not attributable to a simple linking effect or to surface-level linguistic similarity between stories. A follow-up experiment revealed that this benefit did not generalize to perceptual memory for individual objects; instead, perceptual encoding facilitated perceptual object memory. These results reveal that deep, extended encoding emphasizing either narrativity or perceptual processing can benefit different aspects of retrieval.
On-chip superconducting microwave frequency divider
We propose an on-chip superconducting microwave frequency divider based on the simultaneous excitation of two superconducting qubits by a single photon. The proposed circuit consists of two flux qubits and three superconducting resonators. A high-frequency photon stored in the input resonator simultaneously excites two qubits through an effective three-body interaction, and the excitation energy is subsequently transferred into two low-frequency resonators. As a result, one high-frequency microwave signal can be divided into two spatially separated low-frequency output signals. The continuous-wave output, conversion efficiency, and controllable pulse outputs of the proposed device are analyzed numerically using the master-equation approach. Compared with conventional nonlinear frequency-conversion schemes, the proposed device naturally generates spatially separated output channels, which may simplify microwave signal routing in superconducting quantum processors.
Race-conscious admissions algorithms and the law
In recent years, colleges and universities have begun to use machine learning (ML) systems to inform admissions decisions. Meanwhile, in the 2023 case Students for Fair Admissions, Inc. v. President and Fellows of Harvard College , the Supreme Court held that colleges and universities may not make admissions decisions “on the basis of race.” These parallel developments—the rise of ML in admissions and the fall of race-based affirmative action—will force educational institutions, and ultimately courts, to confront the difficult question of what it means for ML systems to differentiate “on the basis of race.” We begin by mapping the Students for Fair Admissions decision onto different uses of race in predictive AI. We distinguish between “first-order” and “second-order” race consciousness at both the training and predictive phases of machine learning, and we argue that each category of race consciousness raises distinct legal and normative issues. We go on to show that the Students for Fair Admissions decision potentially permits—and even endorses—certain forms of race consciousness. Our analysis is grounded in the observation that the process of developing ML-based systems enables policymakers to calibrate decision making algorithms much more precisely and explicitly in response to specific criticisms of race-conscious affirmative action.
Engineering short-range correlation in bi-disperse self-assembled photonic structures
Short-range correlations in disordered photonic structures composed of sub-micrometer scatterers can give rise to correlation-driven photonic gaps. However, methods to systematically tune such structural correlations remain limited. Here, we investigate that the scatterer size ratio (β) in self-assembled bi-disperse photonic structures provides a pathway to engineer short-range correlations and hence the photonic gaps. Correlation-induced reflectivity peaks, accompanied by simultaneous dips in both the transport (lt) and scattering (ls) mean free paths, emerge over the range of 0.77 < β < 0.83. Further reduction in β value suppresses short-range correlations and consequently eliminates the photonic gap. Pair correlation and structure factor analyses reveal that the observed transition from well-resolved to vanishing gaps is driven by the breakdown of short-range correlations. Experimentally extracted lt and ls further show that enhanced backscattering is associated with short-range correlations. Moreover, tuning the β value not only shifts the gap wavelength but also modulates its strength, as supported by finite-difference time-domain simulations. Together, these results advance disorder-enabled design strategies for photonic materials, enabling precise control of scattering and bandgap characteristics.
When does exercise hinder rather than halt tumor progression? The decisive role of immunotherapy responsiveness
Inverse design of elastic metamaterial plate with all-polarized odd-order topological bandgaps
Elastic metamaterial plates support rich polarization-components, high energy density, and strong miniaturization potential. However, most elastic metamaterial designs target only a single polarization, often the out-of-plane modes after modal separation, making it challenging to realize overlapping in-plane and out-of-plane complete bandgaps using conventional forward design. Here, we develop an inverse-design framework that treats in-plane and out-of-plane dispersion branches as distinct objectives. By combining the multi-objective genetic algorithm NSGA-II with a pixel-encoded topology representation, we generate unit-cells that achieve user-prescribed all-polarized bandgap combinations. The optimized designs exhibit clear polarization-selective band separation, enlarged common bandgaps, and odd-order bandgap topology for both polarizations. By lattice translation, we further obtain topological counterparts with opposite bulk polarization. Ribbon and finite-waveguide calculations confirm all-polarized topological interface modes.
Catalysis does not explain expansin-mediated wall creep: Distinguishing catalytic ERPs from creep-active expansins
Tracking walls movement in magnetic wires. Can the shape of the walls be accurately revealed experimentally?
Soft amorphous magnetic wires are promising magnetic materials for applications as magnetic logic devices, random access memory, integrated circuits, hard disks, and spintronic devices. These applications are based on the very fast propagation of a single domain wall inside the magnetic wires. Determination of the domain wall structure is an important task due to the role that it plays in the functioning of different applications. In this paper, we analyze the use of the induced signal in a Sixtus–Tonks type experiment to obtain information about domain wall geometry. Although this is a method often used in the scientific literature, we show that without considering the parameters of the detection coil the obtained results can be affected by significant errors. The physical origins of the induced signal are analyzed in this paper, calculating the signal by means of the effective surface and volume magnetization currents, for different types of domain walls. We show that the induced signal is due in part to the domain wall, and in part to the two uniformly magnetized domains separated by the domain wall. The latter contribution exists whatever the shape of the wall, and it can even almost cover the wall contribution. The results are important for understanding and controlling magnetic switching in magnetic wires for future applications. The systematic fundamental theoretical and modeling study presented in this paper is showing the capability and the limits of the inductometric wall detection tools to understand the observed signals measured in experimental setups used in various applications.
Reply to Cosgrove: Catalytic activity in expansin-like proteins does not explain wall creep but raises a flag
Re-evaluating the applicability of Peierls–Nabarro models for GaAs: A strain-based criterion
Classic Peierls–Nabarro (PN) model is widely regarded as inadequate for estimating Peierls stress in ceramics and semiconductors, driving the development of improved variants such as the D-PN and local misfit energy (LME) models. However, we find that this view does not apply to GaAs. This work establishes a strain-based criterion for determining the applicable conditions of different PN models. Using the 1/2[1¯10](111) screw dislocation in GaAs as the research system, we apply both the classic generalized stacking faults (GSF) model and the LME model to calculate the restoring force and solve the PN equation. LME simulations reveal that an inelastic strain dominates the shear process of a [1¯10](111) slip system, satisfying the conditions for the classic GSF model. The Peierls stress predicted by the GSF model exhibits good agreement with the experimental data. These results demonstrate that the classic model remains effective when the dislocation core is dominated by the inelastic strain. In contrast, introducing excessive elastic strain via improved models yields unphysical predictions. This work provides a practical protocol for selecting the appropriate PN model to simulate dislocations in ceramics and semiconductors.
Recent advances in tuning molecular transport properties in metal-organic frameworks and covalent-organic frameworks for few sorption related applications: A mini review
Transport properties of metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) play important roles in their performance in various applications. This Mini Review discusses how transport properties, e.g., diffusivity and transport resistance, might be manipulated in these materials for optimal performance. We focused on three applications, e.g., membrane separation, adsorption, and catalysis. For membrane separation, examples will be given to illustrate different strategies applied to address the limited diffusion selectivity issues, high transport resistance from thick membranes, and the challenges for scaling up high-performance membranes. For adsorption, strategies will be explained on how to achieve ultrahigh adsorption selectivity (molecular sieving), leveraging kinetic selectivity for separations with low equilibrium adsorption selectivity, and addressing the slow adsorption kinetics issues. The importance of pore size engineering will be discussed for applying MOFs and COFs either as the catalysts or catalyst supports. Last, how computational chemistry and machine learning for accelerating materials research will also be touched. This article will give readers a general idea of recent progress on tuning the transport properties of MOFs and COFs and how innovative strategies could be leveraged to achieve the desirable process outcomes.
Reply to Zhou et al.: Exercise, caloric restriction, and immunotherapy responsiveness in 4T1 breast cancer progression
Supersonic water jets driven by exploding conical wire arrays and their impact on aluminum targets
This work investigates the formation of supersonic water jets produced by exploding conical wire arrays and their impact on aluminum targets of different thicknesses. Experiments are performed using the pulsed power driver at beamline ID19 of the European Synchrotron Radiation Facility (ESRF), with the wire arrays driven by a 95 kA electrical discharge with a current rise time of ∼550 ns. Jet formation is diagnosed using multi-frame enhanced phase-contrast x-ray radiography at a pulse repetition rate of 5.68 MHz. Jet velocities in the range of 1450 − 1820 m/s are achieved, and the radiographs suggest a strongly density-depleted, possibly hollow jet core surrounded near its base by denser liquid set into motion during jet formation. For a jet impacting a thick aluminum target, radiographs show a high-density stagnation region at the impact position and radial water splashing caused by redirection of the incoming liquid flow along the target surface, followed by atomization of the spreading liquid. Axisymmetric compressible multiphase Navier–Stokes simulations estimate a peak impact pressure of ∼1.4 GPa, associated with initial water-hammer loading. In separate jet penetration experiments on thin aluminum foils, the material deformation rate decreases with increasing foil thickness, and foil perforation is observed up to a target thickness of 0.3 mm, consistent with analytical estimates based on a shear-failure criterion.
Multi-valley evolution and corresponding effects on thermoelectric transport in low-symmetry heterostructure GeS/GeSe
Layered germanium chalcogenides are promising thermoelectric (TE) materials, yet it remains difficult to achieve synergistic optimization of electrical and thermal transport in single-component materials. The complex multi-valley characteristics in low-symmetry van der Waals heterostructure GeS/GeSe provide a new strategy for optimizing TE transport. Therefore, this work systematically explores the evolution of multi-valley characteristics in bilayer GeS, bilayer GeSe, and GeS/GeSe, revealing its impact on TE transport properties. Since there exist 2 (5) valleys near the valence band maximum (conduction band minimum), the optimal power factor S2σ for p-type and n-type GeS/GeSe reaches 88.5 and 149.6 μW cm−1 K−2. For phonon transport, due to the distinct differences of projected phonon density of states overlap in the low-frequency region, which is closely related to the cooperativity of phonon vibrational modes, the average phonon relaxation times τq¯ differ substantially among the three structures. At 300 K, the average lattice thermal conductivities κL¯ of bilayer GeS, bilayer GeSe, and GeS/GeSe are 3.72, 8.57, and 7.52 W m−1 K−1, respectively, which are relatively low among similar semiconductors. Overall, GeS/GeSe possesses superior TE performance due to the presence of multi-valley degeneracy and low κL. This work clarifies the complex multi-valley distribution and evolution mechanism, uncovers the intrinsic link between multi-valley degeneracy and TE performance, and offers theoretical guidance for optimizing TE properties via low-symmetry structure engineering and multi-valley modulation.
Major Adverse Cognitive Events (MACE-Cog): A New “MACE” Framework for Cognitive Outcomes in Cardiovascular Diseases and Stroke
Cardiovascular diseases, particularly stroke, are leading causes of dementia. Several common cardiac interventions such as coronary artery bypass grafting and transcatheter aortic valve implantation are also associated with cognitive decline. However, cognitive outcomes continue to be poorly collected in clinical trials of cardiovascular diseases. In this article, we review the limitations of current approaches to cognitive assessment in cardiovascular disease studies. When assessed, there is wide variation in cognitive tasks used, and tasks have limited population-specific validation, are subject to floor and ceiling effects, and may suffer from sociocultural and linguistic biases. Many tasks are not available in multilingual formats and often rely on face-to-face testing with trained coordinators. All conventional cognitive outcomes in cardiovascular trials are associated with substantial incompletion, especially among older and more impaired individuals, the very people most important to capture. This nonrandom missingness generates survivor and attrition biases, an unacceptable situation for any trial outcome. Last, the meaning of measured cognitive outcomes is often unclear for patients, caregivers, clinicians, and regulators. To help address these limitations, we propose a new framework, major adverse cognitive events (MACE-Cog), to better capture cognitive outcomes in stroke and other cardiovascular populations. As an inclusive construct reflecting the multidimensional nature of cognitive decline, major adverse cognitive events do not rely solely on performance on cognitive testing but also consider inability to complete cognitive testing due to cognitive-behavioral factors, reported symptoms of cognitive decline, impairment in activities of daily living as a result of cognitive impairment, new clinical diagnoses of dementia, and care home admission. We hope that the proposed composite outcome and multiple use cases presented spark progress in cardiovascular research toward more inclusive approaches to the study of cognitive outcomes that move beyond the confines of cognitive tests alone.
CD40-TRAF2/3/5 Signaling Promotes Cardiac Repair by Mediating Macrophage Efferocytosis After Myocardial Infarction
BACKGROUND: After myocardial infarction (MI), macrophage-mediated clearance of dead cells, a process known as efferocytosis, represents a pivotal role in tissue remodeling. Efficient efferocytosis contributes to rescuing neighboring viable cardiomyocytes, drives the phenotypic transition of reparative macrophages, and facilitates the resolution of inflammation. In this study, we explored the roles of CD40 and the signals transduced by its 2 downstream adaptor-protein binding sites (TRAF2/3/5 and TRAF6) in the cardiac macrophage efferocytosis after MI. METHODS: Systemic, myeloid- and macrophage-specific CD40-deficient mice were used to determine the functional significance of CD40 during post-MI repair. The effects of CD40 on macrophages functional states were evaluated with single-cell RNA sequencing (scRNA-seq). Flow cytometry, immunofluorescence staining, Western blot, and ELISA were used to assess the efferocytosis and inflammatory status of macrophages after MI. CD40-TRAF2/3/5 −/− and CD40-TRAF6 −/− mice were used to explore the roles of CD40 downstream signaling intermediates in MI and macrophage efferocytosis. RESULTS: The expression level of CD40 was increased remarkably from 3 to 7 days after MI. Myeloid-derived macrophages emerged as the dominant population expressing CD40. CD40 deficiency resulted in an augmented infarct size and compromised cardiac function after MI. Further investigations demonstrated that CD40 deficiency led to a notable decline in macrophage efferocytosis, which is associated with a reduced abundance of cluster 0 cells, identified by scRNA-seq, representing the precursor of reparative macrophages. Moreover, scRNA-seq indicated that CD40 + macrophages could be classified primarily into 2 distinct cell subsets: 1 subset was associated mainly with efferocytosis functions, and the other was involved predominantly in immune-inflammatory responses. Direct activation of CD40 failed to upregulate macrophage efferocytosis but instead induced a proinflammatory state. These implied differential effects of the signals transduced by the 2 TRAF binding sites (TRAF2/3/5 and TRAF6) downstream of CD40 on efferocytosis. Findings from CD40-TRAF2/3/5 −/− and CD40-TRAF6 −/− mice confirmed that the CD40-TRAF2/3/5 signaling served as a crucial determinant in mediating CD40-related efferocytosis. STAT6 was identified as a key downstream factor in this process. Adenovirus-mediated gene transfer to overexpress a CD40 variant retaining TRAF2/3/5 binding site but lacking the TRAF6 in cardiac macrophages led to improvements in cardiac function and macrophage efferocytosis after MI. CONCLUSIONS: Our study established a pivotal positive role of macrophage CD40 in post-MI repair by facilitating macrophage efferocytosis. Specifically, TRAF2/3/5 rather than TRAF6 serves as the crucial signaling pathway that mediates CD40-associated efferocytosis.