Predictions Of Theory Research Articles

Three-dimensional metamaterials based on discrete assembly to customize thermal expansion response under temperature stimuli

Three-dimensional (3D) metamaterials with the customizable thermal response under the temperature stimuli are of great necessity for engineering applications. However, current researches usually focused on the design strategy of these metamaterials and lack the integrated strategy throughout design and fabrication. Here, an integrating design-manufacturing-response workflow is established to develop a series of 3D metamaterials. Specifically, a construction strategy, which is based on discrete assembly, mortise-and-tenon structures and transition cube blocks, is proposed to connect the dissimilar and out-of-plane struts. Then, totally three classes of 3D complicated metamaterials made of Aluminum and Invar alloys are fabricated to export multi-directional customizable coefficient of thermal expansion (CTE). Additionally, the theory prediction, simulations and experiments are conducted to characterize the thermal responses. The results suggest that through reasonably modulating the geometrical parameters, all the fabricated metamaterials are capable of reaching the CTEs across from large positive, near-zero and negative values. Besides, the metamaterials PC and UN present the unidirectional customizable CTEs (-12.72 × 10-6 ∼ +42.73 × 10-6/°C and -12.54 × 10-6 ∼ +41.51 × 10-6/°C, respectively), while the metamaterials FS show the transversal isotropic CTEs (-10.89 × 10-6 ∼ +34.66 × 10-6/°C) and IS have the isotropic customizable CTEs (-10.18 × 10-6 ∼ +34.58 × 10-6/°C). Overall, these originally developed metamaterials open a new avenue for customizing the targeted thermal responses for engineering applications, where the thermal expansion is of high significance.

Exploring the kinetics and thermodynamics of TiFe0.8CrxMn0.2-x hydrogen storage alloys

The influence of Fe substitution by Cr and/or Mn on the hydrogenation properties of TiFe0.8CrxMn0.2-x hydrogen storage alloys was explored for several substitution amounts (x = 0, 0.05, 0.1, 0.15 and 0.2). The synthesized alloys consisted of a dominant TiFe (cubic, CsCl-type) and a secondary C14 Laves phase (hexagonal, MgZn2-type). The increase in Cr content, and consequently in C14 Laves phase fraction, improved the first hydrogenation kinetics and the hydrogen intake after activation (1.60–1.70 wt% H2 at 30 °C within 3 min), while substantially stabilizing the monohydride phase. The reaction enthalpy obtained from pressure-composition-temperature (PCT) curves hence increased along Cr substitution, ranging 24.35 ± 0.34 to 35.39 ± 1.00 kJmol-1 for the absorption, and 31.08 ± 1.39 to36.34 ± 1.99 kJmol−1for the desorption (absolute values). These experimental findings were in good agreement with density functional theory (DFT) predictions performed across the explored composition space. The estimated entropy values on the other hand remained nearly constant, fluctuating between 84.28 ± 4.76 and 102.64 ± 3.12 J mol−1 H2 K−1 for the absorption, and 100.46 ± 9.71 up to 105.45 ± 4.31 J mol−1 H2 K−1 for the desorption. Solid-gas reaction modeling showed that, under practical service conditions, the hydrogenation of activated alloys was interface-controlled followed by diffusion-controlled mechanisms as the reaction proceeded.

Interpersonal theory of suicide predictability of suicidal behaviour: European Spanish validation of the acquired capability for suicide scale-fearlessness about death

Interpersonal theory of suicide predictability of suicidal behaviour: European Spanish validation of the acquired capability for suicide scale-fearlessness about death

Magnetothermal effect and first-principles calculations of Zn-doped Mn5Ge3-based alloys

This study investigates the compound system Mn5−xZnxGe3 (x = 0.1, 0.2, and 0.3) through experimental investigations and theoretical calculations. Zn doping lowers the Curie temperature and magnetic entropy change of Mn5−xZnxGe3 alloys. Analysis of phenomenological curves, including Landau theories, normalized curves, and Arrott curves during the study of isothermal magnetization curves, reveals a second-order phase transition in this system. Through an extensive investigation of critical behavior using critical isotherm curves and the Kouvel–Fisher (KF) method, the consistency and reliability of these critical indices are validated by the prediction of the scaling theory in the critical region. By scaling the dependence of |ΔSM| on M and applying crucial exponen21t values, an efficient new approach is utilized to calculate the spontaneous magnetization that agrees well with the values deduced from the KF method. Additionally, first-principles calculations reveal that the Mn atoms' 3d orbitals are more significantly close to the Fermi energy level, with Zn doping generally reducing both the electronic density of states and the total magnetic moment of the Mn 3d orbitals. Consequently, the introduction of Zn leads to a decrease in the Mn–Mn atom exchange coupling, resulting in a deterioration of the total exchange interaction. This phenomenon also explains the decrease in the Curie temperature TC due to Zn doping, aligning with experimental observations.

Notes on peculiarities of the Schwinger-DeWitt technique: One-loop double poles, total-derivative terms, and determinant anomalies

We discuss peculiarities of the Schwinger-DeWitt technique for quantum effective action, associated with the origin of dimensionally regularized double-pole divergences of the one-loop functional determinant for massive Proca model in a curved spacetime. These divergences have the form of the total-derivative term generated by integration by parts in the functional trace of the heat kernel for the Proca vector field operator. Because of the nonminimal structure of second-order derivatives in this operator, its vector field heat kernel has a nontrivial form, involving the convolution of the scalar d’Alembertian Green’s function with its heat kernel. Moreover, its asymptotic expansion is very different from the universal predictions of Gilkey-Seeley heat kernel theory because the Proca operator violates one of the basic assumptions of this theory—the nondegeneracy of the principal symbol of an elliptic operator. This modification of the asymptotic expansion explains the origin of double-pole total-derivative terms. Another hypostasis of such terms is in the problem of multiplicative determinant anomalies—lack of factorization of the functional determinant of a product of differential operators into the product of their individual determinants. We demonstrate that this anomaly should have the form of total-derivative terms and check this statement by calculating divergent parts of functional determinants for products of minimal and nonminimal second-order differential operators in curved spacetime. Published by the American Physical Society 2024

Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension.

Predictive processing in parietal, temporal, frontal, and sensory cortex allows us to anticipate future meanings to maximize the efficiency of language comprehension, with the temporoparietal junction (TPJ) and inferior frontal gyrus (IFG) thought to be situated towards the top of a predictive hierarchy. Although the regions underpinning this fundamental brain function are well-documented, it remains unclear how they interact to achieve efficient comprehension. To this end we recorded functional magnetic resonance imaging (fMRI) in 22 participants (11 males) while they comprehended sentences presented part-by-part, in which we manipulated the constraint provided by sentential contexts on upcoming semantic information. Using this paradigm, we examined the connectivity patterns of bilateral TPJ and IFG during anticipatory phases (i.e., before the onset of targets) and integration phases (i.e., after the onset of targets). When upcoming semantic content was highly predictable in strong-constraint contexts, both left TPJ and bilateral IFG showed stronger visual coupling, while right TPJ showed stronger connectivity with regions within control, default mode, and visual networks, including IFG, parahippocampal gyrus, posterior cingulate, and fusiform gyrus. These connectivity patterns were weaker when predicted semantic content appeared, in line with predictive coding theory. Conversely, for less predictable content, these connectivity patterns were stronger during the integration phase. Overall, these results suggest that both top-down semantic prediction and bottom-up integration during predictive processing are supported by flexible coupling of frontoparietal regions with control, memory, and sensory systems.Significance Statement Recent work has revealed the neural basis of predictive language comprehension. However, it remains unclear how brain regions change their connectivity in a dynamic fashion to support comprehension in highly predictive and less predictive contexts. Here, we show that stronger frontoparietal connectivity with cognitive control, memory, and sensory areas supports top-down prediction generation in strong-constraint contexts; these connectivity patterns are reduced when the anticipated information appears. This pattern is reversed when upcoming sensory input is unpredictable; connectivity is stronger after word inputs have been presented, allowing semantic integration with preceding low-constraint context. Our findings suggest that both top-down semantic prediction and bottom-up semantic integration in language comprehension rely upon diverse functional coupling of higher-order frontoparietal regions with other brain systems.

Dielectric microsphere sizing using fluorescence microscopy of whispering gallery mode resonances from adsorbed dyes

Whispering gallery mode (WGM) resonances in dielectric microspheres are very sensitive to their size and environment, which can be used for sensing but also as an indirect proxy to determine their size. By coating them with suitable fluorescent dyes and using fluorescence microscopy, we show that the WGM resonances of individual microspheres in solution can be easily studied with a high throughput. Brownian motion ensures that a representative sample is probed over time in the scattering volume. To analyze these WGM-imprinted fluorescent spectra, we propose a simple algorithm based on monitoring the spacing between resonances and comparing it to Mie theory predictions to infer their size. This allows us to measure the size distribution of typical polystyrene microsphere solutions. We also discuss the potential effects of dye concentration and choice of particle refractive index on the analysis. This method can be used, for example, for quality-testing microsphere solutions.

Nonparametric Predictive Inference for Discrete Lifetime Data

This paper presents nonparametric predictive inference for discrete lifetime data. While lifetimes are mostly treated as continuous random variables in statistics, there are scenarios where time observations are recorded as discrete values, for example, in actuary, where lifetimes are often recorded as integers in years. The presented method provides lower and upper probabilities for a variety of events of interest involving discrete lifetimes, with examples provided for illustration. Furthermore, the discrete-time situation is considered for inference of the reliability of systems, with discrete-time data for components of different types and using the survival signature to combine inference on components’ reliability to quantify the overall system reliability.

Active fluctuations of axoneme oscillations scale with number of dynein motors

Fluxes of energy generate active forces in living matter, yet also active fluctuations. As a canonical example, collections of molecular motors exhibit spontaneous oscillations with frequency jitter caused by nonequilibrium phase fluctuations. We investigate phase fluctuations in reactivated Chlamydomonas reinhardtii axonemes, which are accessible to direct manipulation. We quantify the precision of axonemal oscillations after controlled chemical removal of dynein motors, providing an experimental test for the theory prediction that the quality factor of motor oscillations should increase with motor number. Our quantification reveals specialized roles of inner and outer arm dynein motors. This supports a model in which inner dyneins serve as master pace-makers, to which outer arm dyneins become entrained, consistent with recent insight provided by structural biology.

Evaluation method of progressive failure of loess slope under shallow coal seam mining

Abstract When mining coal under loess gully, the surface slopes may exhibit significant discontinuous failure characteristics such as local failure and landslides. These characteristics, composed of failure and non-failure regions, form an essentially unstable mechanical system. This makes it difficult to obtain a true solution using continuous medium mechanics, greatly increasing the complexity of slope failure evaluation and management. To accurately evaluate the degree of slope failure during mining, clarify its mechanism, and propose appropriate management measures, the Ningtiaota Coal Mine in northern Shanxi was selected as the study area. Based on the unbalanced thrust method and the probability integral settlement prediction theory, and utilizing numerical calculations, we proposed a method for evaluating and managing slope failure in coal mining under loess gully. This method describes the changes in slope failure during mining through the deformation failure ratio of the slip surface and quantifies the degree of slope failure at different mining times, revealing the full process stability evolution characteristics of points and surfaces as the critical state of the slope progresses. When the deformation failure ratio of the slip surface is ≥1, it indicates overall slope failure and the beginning of slope sliding instability; when the deformation failure ratio of the slip surface is <1, it indicates local slope failure. Based on this, corresponding management methods were proposed according to the mechanical distribution characteristics of the slope slip surface in different stress areas. The research results were applied in the Ningtiaota Coal Mine in northern Shanxi, yielding positive outcomes.

All-optical switching in nonlinear topological waveguide arrays.

Photonic topological states are prospective in integrated optical devices due to their robustness to perturbations and defects. When taking into account the nonlinear effects of the system, the functionality of topological photonics can be further enhanced. Here, we investigated the interplay between topological edge states and nonlinear effects based on the Su-Schrieffer-Heeger (SSH) model. Relying on the theory prediction that topological edge states would shift upward under the action of nonlinearity, two types of optical switching are designed and experimentally realized in femtosecond laser direct-write waveguide arrays. This work provides a new, to the best of our knowledge, approach to preparing all-optical switches and offers a new perspective on the application of nonlinearity in topological optical devices.

Self-consistent Strong Screening Applied to Thermonuclear Reactions

Self-consistent strong plasma screening around light nuclei is implemented in the Big Bang nucleosynthesis (BBN) epoch to determine the short-range screening potential, e ϕ(r)/T ≥ 1, relevant for thermonuclear reactions. We numerically solve the nonlinear Poisson–Boltzmann equation incorporating Fermi–Dirac statistics, adopting a generalized screening mass to find the electric potential in the cosmic BBN electron–positron plasma for finite-sized α particles (4He++) as an example. Although the plasma follows Boltzmann statistics at large distances, Fermi–Dirac statistics is necessary when work performed by ions on electrons is comparable to their rest-mass energy. While self-consistent strong screening effects are generally minor owing to the high BBN temperatures, they can enhance the fusion rates of high-Z (Z > 2) elements while leaving fusion rates of lower-Z (Z ≤ 2) elements relatively unaffected. Our results also reveal a pronounced spatial dependence of the self-consistent strong screening potential near the nuclear surface. These findings about the electron–positron plasma’s role refine BBN theory predictions and offer broader applications for studying weakly coupled plasmas in diverse cosmic and laboratory settings.

Proton quantal delocalization and H/D translocations in (MeOH)nH+ (n = 2, 3).

In this study, we present results from path integral molecular dynamics simulations that describe the characteristics of the quantum spatial delocalizations of protons participating in OH bonds in (MeOH)2H+ and in (MeOH)3H+. The characterization was carried out by examining the overall structures of the corresponding isomorphic polymers. To introduce full flexibility in the force treatment, we have adopted a neural network fitting procedure based on second-order Møller-Plesset perturbation theory predictions. For the dimer case, we found that the spatial extent of the shared connective proton can be portrayed in terms of a prolate-like structure with typical dimensions of ∼0.1 Å. On the other hand, the dangling polymers lie confined within a thin spherical layer, spread over length scales of the order of ∼0.25 Å. In contrast, connective protons in (MeOH)3H+ exhibit larger delocalizations along the O-H bond and more localized ones along perpendicular directions, compared to their dangling counterparts. We also examined the characteristics of the relative propensities of H and D isotopes to be localized in dangling and connective positions. Physical interpretations of the different thermodynamic trends are provided in terms of the local geometrical characteristics and of the strengths of the corresponding intermolecular connectivities.

Effects of surface roughness on the drag coefficient of finite-span cylinders freely rolling on an inclined plane

We present an experimental investigation aimed at understanding the effects of surface roughness on the time-mean drag coefficient ( $\bar {C}_{D}$ ) of finite-span cylinders ( $\text {span/diameter} = \text {aspect ratio}$ , $0.51 \le AR \le 6.02$ ) freely rolling without slip on an inclined plane. While lubrication theory predicts an infinite drag force for ideally smooth cylinders in contact with a smooth surface, experiments yield finite drag coefficients. We propose that surface roughness introduces an effective gap ( $G_{eff}$ ) resulting in a finite drag force while allowing physical contact between the cylinder and the plane. This study combines measurements of surface roughness for both the cylinder and the plane panel to determine a total relative roughness ( $\xi$ ) that can effectively describe $G_{eff}$ at the point of contact. It is observed that the measured $\bar {C}_{D}$ increases as $\xi$ decreases, aligning with predictions of lubrication theory. Furthermore, the measured $\bar {C}_{D}$ approximately matches combined analytical and numerical predictions for a smooth cylinder and plane when the imposed gap is approximately equal to the mean peak roughness ( $R_p$ ) for rough cylinders, and one standard deviation peak roughness ( $R_{p, 1\sigma }$ ) for relatively smooth cylinders. As the time-mean Reynolds number ( $\overline {Re}$ ) increases, the influence of surface roughness on $\bar {C}_{D}$ decreases, indicating that wake drag becomes dominant at higher $\overline {Re}$ . The cylinder aspect ratio ( $AR$ ) is found to have only a minor effect on $\bar {C}_{D}$ . Flow visualisations are also conducted to identify critical flow transitions and these are compared with visualisations previously obtained numerically. Variations in $\xi$ have little effect on the cylinder wake. Instead, $AR$ was observed to have a more pronounced effect on the flow structures observed. The Strouhal number ( $St$ ) associated with the cylinder wake shedding was observed to increase with $\overline {Re}$ , while demonstrating little dependence on $AR$ .

Longitudinal and Lateral Cooperative Control of Preview Intelligent Vehicles with Stabilization

Aiming at the highly nonlinear and coupled longitudinal and transverse motion control problems of intelligent vehicle path tracking control, a new method of preview MPC path tracking control is proposed by combining visual preview model and model predictive control theory. The method first locally linearizes the nonlinear prediction model and transforms the MPC optimization solution problem into a quadratic programming problem. The longitudinal vehicle speed and the preview distance are then treated as known in each control time domain, an exponential model is applied to characterize the longitudinal vehicle speed, and a transverse angular velocity-center of mass lateral deflection (r-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r-\\beta $$\\end{document}) phase plane plot is applied to characterize the vehicle stability. Finally, proportional integral differential stability controller is designed to improve vehicle stability. Simulation and test results show that: Compared with the MPC tracking control method, the preview MPC control method proposed in this paper can optimize the vehicle tracking performance on different adhesion coefficient road surfaces and improve the tracking accuracy, and this enhancement effect is more obvious at high speeds.

Prediction in reading: A review of predictability effects, their theoretical implications, and beyond.

Historically, prediction during reading has been considered an inefficient and cognitively expensive processing mechanism given the inherently generative nature of language, which allows upcoming text to unfold in an infinite number of possible ways. This article provides an accessible and comprehensive review of the psycholinguistic research that, over the past 40 or so years, has investigated whether readers are capable of generating predictions during reading, typically via experiments on the effects of predictability (i.e., how well a word can be predicted from its prior context). Five theoretically important issues are addressed: What is the best measure of predictability? What is the functional relationship between predictability and processing difficulty? What stage(s) of processing does predictability affect? Are predictability effects ubiquitous? What processes do predictability effects actually reflect? Insights from computational models of reading about how predictability manifests itself to facilitate the reading of text are also discussed. This review concludes by arguing that effects of predictability can, to a certain extent, be taken as demonstrating evidence that prediction is an important but flexible component of real-time language comprehension, in line with broader predictive accounts of cognitive functioning. However, converging evidence, especially from concurrent eye-tracking and brain-imaging methods, is necessary to refine theories of prediction.

The Diffusion Tensor of Protons at 1 au: Comparing Simulation, Observation, and Theory

The natural variation in plasma parameters observed at 1 au can lead to a variation in transport parameters, such as diffusion and drift coefficients, for energetic charged particles of solar and galactic origin. Given the importance of these parameters to particle transport studies, this variation is investigated through test particle simulations over a range of energies in the presence of simulated turbulence with properties corresponding to an ensemble of observed turbulence conditions at Earth. The resulting transport coefficients are then compared with observational estimates from the literature, as well as the predictions of several scattering theories. Parallel and perpendicular mean free paths are shown to vary widely, for the former in agreement with prior observational estimates, but not for the latter. Furthermore, a large disparity between the predictions of theory and the simulation results is noted for the perpendicular mean free path. As such, these results indicate that particle transport studies, particularly predictive ones, need to take into account this natural variation in transport coefficients.

Learning probability distributions of sensory inputs with Monte Carlo predictive coding.

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. These frameworks were brought together by variational filtering that introduced neural sampling to predictive coding. Here, we consider a variant of variational filtering for static inputs, to which we refer as Monte Carlo predictive coding (MCPC). We demonstrate that the integration of predictive coding with neural sampling results in a neural network that learns precise generative models using local computation and plasticity. The neural dynamics of MCPC infer the posterior distributions of the latent states in the presence of sensory inputs, and can generate likely inputs in their absence. Furthermore, MCPC captures the experimental observations on the variability of neural activity during perceptual tasks. By combining predictive coding and neural sampling, MCPC can account for both sets of neural data that previously had been explained by these individual frameworks.

Reconciling theories of dominance with the relative rates of adaptive substitution on sex chromosomes and autosomes.

The dominance of beneficial mutations is a key evolutionary parameter affecting the rate and genetic basis of adaptation, yet it is notoriously difficult to estimate. A leading method to infer it is to compare the relative rates of adaptive substitution for X-linked and autosomal genes, which-according to a classic model by Charlesworth et al. (1987)-is a simple function of the dominance of new beneficial mutations. Recent evidence that rates of adaptive substitution are faster for X-linked genes implies, accordingly, that beneficial mutations are usually recessive. However, this conclusion is incompatible with leading theories of dominance, which predict that beneficial mutations tend to be dominant or overdominant with respect to fitness. To address this incompatibility, we use Fisher's geometric model to predict the distribution of fitness effects of new mutations and the relative rates of positively selected substitution on the X and autosomes. Previous predictions of faster-X theory emerge as a special case of our model in which the phenotypic effects of mutations are small relative to the distance to the phenotypic optimum. But as mutational effects become large relative to the optimum, we observe an elevated tempo of positively selected substitutions on the X relative to the autosomes across a broader range of dominance conditions, including those predicted by theories of dominance. Our results imply that, contrary to previous models, dominant and overdominant beneficial mutations can plausibly generate patterns of faster-X adaptation. We discuss resulting implications for genomic studies of adaptation and inferences of dominance.

Uncovering the electrochemical stability and corrosion reaction pathway of Mg (0001) surface: Insight from first-principles calculation

An understanding of the anomalously enhanced hydrogen evolution reaction (HER) of magnesium (Mg) under anodic polarisation in aqueous corrosion is paramount for a predictive theory of its corrosion and metal electrocatalysis. Previous theoretical and experimental studies have proposed that sub-surface hydride phases play a role in this behaviour but the underlying atomic mechanisms remain unclear. By constructing theoretical surface Pourbaix diagrams, based on density functional theory (DFT) calculations, we have identified the atomic structure of a sub-surface hydride phase on the Mg (0001) surface that remains electrochemically stable under significant anodic overpotentials across a wide pH range. Specifically, this stability persists up to 0.38 VSHE under mildly alkaline conditions (e.g., pH = 8), thus providing thermodynamic support for the proposed hydride-enhanced HER under anodic conditions. Reaction barrier analysis establishes that the proposed sub-surface hydride phase could promote anodic HER via a Heyrovsky pathway, based on hydrogen outward diffusion, with an energy barrier of 1.54 eV as the rate-limiting step, showing an anodic characteristic and significantly favouring external anodic polarisation. Furthermore, we have established that the surface adsorption condition, contingent on both the pH and potential, significantly influences the mechanism and kinetics of the initial corrosion of Mg.