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Abstract Cavity electromagnonic system, which simultaneously consists of cavities for photons, magnons (quanta of spin waves), and acoustic phonons, provides an exciting platform to achieve coherent energy transduction among different physical systems down to single quantum level. Here we report a dynamical phase-field model that allows simulating the coupled dynamics of the electromagnetic waves, magnetization, and strain in 3D multiphase systems. As examples of application, we computationally demonstrate the excitation of hybrid magnon-photon modes (magnon polaritons), Floquet-induced magnonic Aulter-Townes splitting, dynamical energy exchange (Rabi oscillation) and relative phase control (Ramsey interference) between the two magnon polariton modes. The simulation results are consistent with analytical calculations based on Floquet Hamiltonian theory. Simulations are also performed to design a cavity electro-magno-mechanical system that enables the triple phonon-magnon-photon resonance, where the resonant excitation of a chiral, fundamental (n = 1) transverse acoustic phonon mode by magnon polaritons is demonstrated. With the capability to predict coupling strength, dissipation rates, and temporal evolution of photon/magnon/phonon mode profiles using fundamental materials parameters as the inputs, the present dynamical phase-field model represents a valuable computational tool to guide the fabrication of the cavity electromagnonic system and the design of operating conditions for applications in quantum sensing, transduction, and communication.

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping; DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods; multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners.

Applied linguists and Communication scholars have mainly adopted different yet complementary approaches to research on language and health communication. Using discourse-analytic (DA) approaches such as conversation analysis and corpus analysis, applied linguists tend to focus on describing and explicating health messaging techniques in naturally occurring interactions. In contrast, communication scholars favour message design (MD) approaches that compare the effects of different message features on participants' perceptions, attitudes, and behavioural intentions. Drawing on examples from interpersonal medical consultation and public health campaign, I demonstrate that an integration of these two paradigms is beneficial for creating a holistic understanding of research on language and health communication. Specifically, the DA paradigm offers rich descriptions of social actions in terms of their organizational structures and linguistic realizations, whereas the MD paradigm quantitatively assess the impact of particular messaging strategies. The challenges and opportunities of this integration are also discussed.

Abstract Current induced spin-orbit torque (SOT) holds great promise for next generation magnetic-memory technology. Field-free SOT switching of perpendicular magnetization requires the breaking of in-plane symmetry, which can be artificially introduced by external magnetic field, exchange coupling or device asymmetry. Recently it has been shown that the exploitation of inherent crystal symmetry offers a simple and potentially efficient route towards field-free switching. However, applying this approach to the benchmark SOT materials such as ferromagnets and heavy metals is challenging. Here, we present a strategy to break the in-plane symmetry of Pt/Co heterostructures by designing the orientation of Burgers vectors of dislocations. We show that the lattice of Pt/Co is tilted by about 1.2° when the Burgers vector has an out-of-plane component. Consequently, a tilted magnetic easy axis is induced and can be tuned from nearly in-plane to out-of-plane, enabling the field-free SOT switching of perpendicular magnetization components at room temperature with a relatively low current density (~1011 A/m2) and excellent stability (> 104 cycles). This strategy is expected to be applicable to engineer a wide range of symmetry-related functionalities for future electronic and magnetic devices.

Self-assembled nanocomposites present opportunities for a range of phase morphologies and desirable properties. Here authors present tuneable self-assembled nanostructures in the SnO2:NiO system; the double-percolated system expands the design of self-assembled oxides for practical applications, e.g. in optoelectronics.

Abstract Excitation of coherent high-frequency magnons (quanta of spin waves) is critical to the development of high-speed magnonic devices. Here we computationally demonstrate the excitation of coherent sub-terahertz (THz) magnons in ferromagnetic (FM) and antiferromagnetic (AFM) thin films by a photoinduced picosecond acoustic pulse. Analytical calculations are also performed to reveal the magnon excitation mechanism. Through spin pumping and spin-charge conversion, these magnons can inject sub-THz charge current into an adjacent heavy-metal film which in turn emits electromagnetic (EM) waves. Using a dynamical phase-field model that considers the coupled dynamics of acoustic waves, spin waves, and EM waves, we show that the emitted EM wave retains the spectral information of all the sub-THz magnon modes and has a sufficiently large amplitude for near-field detection. These predictions indicate that the excitation and detection of sub-THz magnons can be realized in rationally designed FM or AFM thin-film heterostructures via ultrafast optical-pump THz-emission-probe spectroscopy.

Abstract The anomalous Hall effect (AHE) is a quantum coherent transport phenomenon that conventionally vanishes at elevated temperatures because of thermal dephasing. Therefore, it is puzzling that the AHE can survive in heavy metal (HM)/antiferromagnetic (AFM) insulator (AFMI) heterostructures at high temperatures yet disappears at low temperatures. In this paper, an unconventional high‐temperature AHE in HM/AFMI is observed only around the Néel temperature of AFM, with large anomalous Hall resistivity up to 40 nΩ cm is reported. This mechanism is attributed to the emergence of a noncollinear AFM spin texture with a non‐zero net topological charge. Atomistic spin dynamics simulation shows that such a unique spin texture can be stabilized by the subtle interplay among the collinear AFM exchange coupling, interfacial Dyzaloshinski–Moriya interaction, thermal fluctuation, and bias magnetic field.

Abstract With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science.

This document provides information about the data availability for a study conducted by a group of authors. The authors' names are listed, and it is stated that the data supporting the findings of the study are available within the article. The document does not provide any further details about the study or its findings. [Extracted from the article]

Fluorescent lamp manufacturing workers have been extensively exposed to mercury (Hg). Our aim was to assess their health risks using several approved occupational health risk assessment methods, and to find out which method was more suitable for identification of occupational health risks. Work locations, and air and urine samples were collected from 530 exposed workers in Zhejiang, China. Based on the calculated exposure doses, health risks and risk ratios (RRs) as health risk indices, were evaluated using: Environmental Protection Agency (EPA), Australian, Romanian, Singaporean, International Council on Mining and Metals (ICMM), and Control of Substances Hazardous to Health (COSHH) methods. Among the workers, 86.0% had higher Hg levels than the Chinese occupational exposure limits of 0.02 mg/m3, and 16.7% urine samples were higher than the biological exposure limits of 35.0 μg/g·creatinine. Among workers at the injection, etc. locations, their average RRs, evaluated by the EPA, COSHH and Singaporean methods were 0.97, 0.76, and 0.60, respectively, and were significantly higher than the ICMM (0.39), Australian (0.30) and Romanian (0.29) methods. The RRs from the Singaporean method showed significant correlations with the urinary Hg levels (P < 0.01). In conclusion, the Singaporean method was more appropriate than the others for health risk evaluation because the excessive risks were significantly associated with urinary Hg levels among the workers.

Abstract Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials.

The reported study compared the impact of four influence strategies (agency assignment, enhanced active choice, deviance regulation marking, and temporal framing) on English- and Spanish-speaking parents' reported intention to vaccinate their children for HPV. An online experiment was conducted to examine the impact of the strategies. In a fractional factorial design, participating parents (N = 1663) were exposed to combinations of influence strategies in text messages presented as reminders they might receive from a healthcare provider about their child's eligibility for the vaccine series. The results indicated small but significant impacts of agency assignment, enhanced active choice, and deviance regulation marking on parents' reported vaccination intentions. The study adds to the research literature on HPV vaccination communication in two important respects. First, it demonstrated how incorporating evidence-based influence strategies into reminder messages can increase parents' vaccination intentions, an important precursor and predictor of actual vaccine uptake. Second, it sets an important precedent by examining the effects of influence strategies on vaccination intentions across different languages. [ABSTRACT FROM AUTHOR]

The common approaches to control of skymions cause significant heat dissipation or require high electric fields near the breakdown threshold. Here, the authors demonstrate electric-field control of skyrmions through strain-mediated magnetoelectric coupling in multiferroic heterostructures.

The Transiting Exoplanet Survey Satellite (TESS) mission searches for new exoplanets. The observing strategy of TESS results in high-precision photometry of millions of stars across the sky, allowing for detailed asteroseismic studies of individual systems. In this work, we present a detailed asteroseismic analysis of the giant star HD 76920 hosting a highly eccentric giant planet ( e = 0.878) with an orbital period of 415 days, using five sectors of TESS light curve that cover around 140 days of data. Solar-like oscillations in HD 76920 are detected around 52 μ Hz by TESS for the first time. By utilizing asteroseismic modeling that takes classical observational parameters and stellar oscillation frequencies as constraints, we determine improved measurements of the stellar mass (1.22 ± 0.11 M _⊙ ), radius (8.68 ± 0.34 R _☉ ), and age (5.2 ± 1.4 Gyr). With the updated parameters of the host star, we update the semimajor axis and mass of the planet as a = 1.165 ± 0.035 au and ${M}_{{\rm{p}}}\sin i=3.57\pm 0.22\,{M}_{\mathrm{Jup}}$ . With an orbital pericenter of 0.142 ± 0.005 au, we confirm that the planet is currently far away enough from the star to experience negligible tidal decay until being engulfed in the stellar envelope. We also confirm that this event will occur within about 100 Myr, depending on the stellar model used.

Deterministic switching of a magnetic vortex with an electric field is challenging because electric fields cannot break time-reversal symmetry. Here we demonstrate, using phase-field simulations, a deterministic switching of the vortex chirality in a triangle-shaped nanomagnet by applying an electric field to its underlying ferroelectric layer. The nanomagnet is juxtaposed with an overlying antiferromagnetic layer to acquire an exchange bias from their interface. The simulations show that such deterministic electrically-driven magnetic vortex chirality switching is enabled by a synergistic effect of the electric-field-induced strain from the ferroelectric, the three-fold in-plane shape anisotropy of the nanomagnet, and the exchange-bias field.

Antibiotic resistance is a serious health threat that healthcare providers must communicate to the public to decelerate its development. Prior studies have shown that linguistic agency assignment is a viable strategy to frame health threats in a way that both conveys their severity and preserves audience members' sense of self-efficacy. In the current study, we examined this messaging strategy in the context of antibiotic resistance. Individuals' perceptions of the threat and efficacy, behavioral intentions, fear appeals, and evaluations of the educational fact sheet were explored. Participants (N = 449) were randomly assigned to one of the eight conditions crossing threat agency (bacteria/human), temporal agency (antibiotic resistance/human) and imagery agency (taking antibiotics/antibiotics). The results revealed that individuals' perceived severity, susceptibility, response efficacy, and self-efficacy were positively associated with their intentions to use antibiotics judiciously. The interaction effects between perceived threat and efficacy predicted behavioral intentions and the persuasiveness of the fact sheet. Relative to bacteria threat agency, human agency assignment led to significantly higher behavioral intentions. Also, readers of the human temporal agency condition reported higher persuasiveness toward the fact sheet than readers of the resistance condition. The implications, limitations, and future research directions of the study are discussed. [ABSTRACT FROM AUTHOR]

Expressing gratitude and patronage in the form of dedication or acknowledgment can be traced back to the 16th century (Görlach, 2004). In the present time, writing an acknowledgment section has become a common practice in scholarly works, such as books, dissertations, theses, and journal articles. Research on acknowledgments as a text type, however, is relatively new. In his pioneering work, Hyland (2003) dubbed dissertation acknowledgments 'a Cinderella genre' because it is often regarded as a taken-for-granted component in a dissertation and has not received due academic attention. Unlike other conventional academic genres that aim to establish claims and reputation, dissertation acknowledgments reflect the reciprocal gift-giving among scholars, constituting 'the most explicitly interpersonal genre of the academy' (Hyland, 2004: 323). In addition to expressing gratitude to others, writers also use the acknowledgments section to build interpersonal relationships with their academic colleagues and/or professional communities (Chan, 2015). In this study, we adopt computerized text analysis to further explore how language patterns mark the interpersonal features in English dissertation acknowledgments. [ABSTRACT FROM AUTHOR]

Abstract Magnetoelectric composites and heterostructures integrate magnetic and dielectric materials to produce new functionalities, e.g., magnetoelectric responses that are absent in each of the constituent materials but emerge through the coupling between magnetic order in the magnetic material and electric order in the dielectric material. The magnetoelectric coupling in these composites and heterostructures is typically achieved through the exchange of magnetic, electric, or/and elastic energy across the interfaces between the different constituent materials, and the coupling effect is measured by the degree of conversion between magnetic and electric energy in the absence of an electric current. The strength of magnetoelectric coupling can be tailored by choosing suited materials for each constituent and by geometrical and microstructural designs. In this article, we discuss recent progresses on the understanding of magnetoelectric coupling mechanisms and the design of magnetoelectric heterostructures guided by theory and computation. We outline a number of unsolved issues concerning magnetoelectric heterostructures. We compile a relatively comprehensive experimental dataset on the magnetoelecric coupling coefficients in both bulk and thin-film magnetoelectric composites and offer a perspective on the data-driven computational design of magnetoelectric composites at the mesoscale microstructure level.

Magnetoelectric effect enables controlling magnetism with an electric field or controlling polarization remotely with a magnetic field, without involving any driving electric currents. Since its discovery in the 1960s, the magnetoelectric effect has constantly been inspiring new fundamental science and the development of fascinating device concepts. This perspective is focused on two types of magnetoelectric devices: an ultralow-heat-dissipation spin memory/logic and a magnetoelectric magnetic field sensor. For each device, the paper first overviews the potential market needs, then discusses several key device attributes and outstanding challenges. An outlook for the development of other magnetoelectric devices is also provided.

OBJECTIVE To evaluate the prospective association between cumulative resting heart rate (cumRHR) and rapid renal function decline (RRFD) in a cohort of individuals aged 60 and older. METHODS In the Tianjin Chronic Kidney Disease Cohort Study, the individuals who underwent three consecutive physical examinations between 2014 and 2017, with estimated glomerular filtration rate (eGFR) greater than 60 mL/min per 1.73 m2 and aged 60 years or older were enrolled. A total of 27,564 patients were prospectively followed up from January 1, 2017 to December 31, 2020. The 3-year cumRHR was calculated. The primary outcome was RRFD, defined as an annualized decline in eGFR of 5 mL/min per 1.73 m2 or greater. Logistic and restricted spline regression models and subgroup analysis were used to investigate the association of cumRHR with RRFD after adjusting for all confounders. RESULTS During a median follow-up of 3.2 years, a total of 4,347 (15.77%) subjects developed RRFD. In fully-adjusted models, compared with the lowest quartile of cumRHR, the odds ratio (OR) for the highest was 1.44 (1.28-1.61), P < 0.001. Furthermore, each 1-standard deviation (27.97 beats/min per year) increment in cumRHR was associated with a 17% (P < 0.001) increased risk of RRFD, with a linear positive correlation (P for non-linear = 0.803). Participants with a 3-year cumRHR = 207 (beats/min) * year (equivalent to = 69 beats/min per year in 3 years) were found to be at a higher risk of RRFD. CONCLUSIONS The cumRHR is significantly associated with a higher risk of RRFD among older adults. These results might provide an effective goal for managing and delaying the decline of renal function in the older adults. [ABSTRACT FROM AUTHOR]