The purpose of the study is to introduce a new Model of Coarticulation, namely, Degree of Articulatory Dispersion. This paper reviews the Models of Coarticulation, and then the validity of the Degree ...of Articulatory Dispersion is proved by finding the correlation between the dispersion of DAD and the slope of LEM in the four groups of Monguor consonants classified by the manner of articulation. The finding shows that there is a significant negative correlation between the dispersion of DAD and the slope of LEM. Therefore, Degree of Articulatory Dispersion can be a valid Model of Coarticulation to measure the degree of coarticulation.
This reprint brings together fifteen articles published in the Special Issue of the journal Atmosphere, entitled “High-Performance Computing Serving Atmospheric Transport & Dispersion Modelling”. ...These articles cover a wide variety of topics related to air quality in urban areas and nature-based solutions to improve it in the context of climate change; impact studies on human health and the environment of facilities and infrastructure projects as well as risk studies; the assessment of emerging threats; and preparations for and responses to emergencies involving toxic, flammable, or explosive atmospheric releases. As the fifteen articles presented here remarkably illustrate, what these contemporary topics have in common is the implementation of multi-scale simulations of atmospheric transport and dispersion by means of physical models of computational fluid dynamics (CFDs), whose potential is enhanced by high-performance computing (HPC). This reprint thus addresses the answers provided by modelling and the most advanced simulations to some societal matters of major interest.
We analyze the universal radiative correction ΔRV to neutron and superallowed nuclear β decay by expressing the hadronic γW-box contribution in terms of a dispersion relation, which we identify as an ...integral over the first Nachtmann moment of the γW interference structure function F3(0). By connecting the needed input to existing data on neutrino and antineutrino scattering, we obtain an updated value of ΔRV=0.02467(22), wherein the hadronic uncertainty is reduced. Assuming other standard model theoretical calculations and experimental measurements remain unchanged, we obtain an updated value of |Vud|=0.97370(14), raising tension with the first row Cabibbo-Kobayashi-Maskawa unitarity constraint. We comment on ways current and future experiments can provide input to our dispersive analysis.
This reprint brings together fifteen articles published in the Special Issue of the journal Atmosphere, entitled “High-Performance Computing Serving Atmospheric Transport & Dispersion Modelling”. ...These articles cover a wide variety of topics related to air quality in urban areas and nature-based solutions to improve it in the context of climate change; impact studies on human health and the environment of facilities and infrastructure projects as well as risk studies; the assessment of emerging threats; and preparations for and responses to emergencies involving toxic, flammable, or explosive atmospheric releases. As the fifteen articles presented here remarkably illustrate, what these contemporary topics have in common is the implementation of multi-scale simulations of atmospheric transport and dispersion by means of physical models of computational fluid dynamics (CFDs), whose potential is enhanced by high-performance computing (HPC). This reprint thus addresses the answers provided by modelling and the most advanced simulations to some societal matters of major interest.
We propose a discretization‐free approach based on the physics‐informed neural network (PINN) method for solving the coupled advection‐dispersion equation (ADE) and Darcy flow equation with ...space‐dependent hydraulic conductivity K(x). In this approach, K(x), hydraulic head, and concentration fields are approximated with deep neural networks (DNNs). We assume that K(x) is given by its values on a grid, and we use these values to train the K DNN. The head and concentration DNNs are trained by minimizing the residuals of the flow equation and ADE and using the initial and boundary conditions as additional constraints. The PINN method is applied to one‐ and two‐dimensional forward ADEs, where its performance for various Péclet numbers (Pe) is compared with the analytical and numerical solutions. We find that the PINN method is accurate with errors of less than 1% and outperforms some conventional discretization‐based methods for large Pe. Next, we demonstrate that the PINN method remains accurate for the backward ADEs, with the relative errors in most cases staying under 5% compared to the reference concentration field. Finally, we show that when available, the concentration measurements can be easily incorporated in the PINN method and significantly improve (by more than 50% in the considered cases) the accuracy of the PINN solution of the backward ADE.
Key Points
Physics‐informed neural network (PINN) method is proposed for forward and backward advection‐dispersion equations
The physics‐informed neural network (PINN) method has several advantages over some grid‐based discretization methods for high Péclet number problems
The physics‐informed neural network (PINN) method is accurate for the considered backward advection‐dispersion equations (ADEs) that otherwise must be treated as computationally expensive inverse problems
Oxide dispersion-strengthened (ODS) alloys possess several advantages such as excellent high-temperature creep strength and oxidation/irradiation resistance. Lanthanide oxides as well as yttria ...(Y2O3) have been proven to act as efficient additives in Fe-based ODS alloys; however, their potential for addition to Ni-based alloys has been limited. In this study, we fabricated four types of Ni-based ODS alloys with the combined addition of Ti and either Y2O3, cerium dioxide (CeO2), lanthanum oxide (La2O3), or gadolinium (III) oxide (Gd2O3) by mechanical alloying (MA) and hot isostatic pressing (HIP). We characterized both MA powders and HIPed samples of each alloy to compare the effect of oxide addition on the microstructure evolution. The results indicated that the addition of La2O3 had the most significant influence on refining oxide dispersoids and delaying recrystallization, resulting in a uniformly distributed ultra-fine grain structure. Based on further investigation of the contributions of various strengthening mechanisms, it was also found that the refinement of both oxide particles and grain size with the addition of La2O3 contributed to significant dispersion strengthening and grain boundary strengthening, leading to the highest Vickers hardness among the present alloys. When CeO2 and Gd2O3 were added, the mechanical properties were comparable, but better than those when Y2O3 was added. This demonstrates that all of the selected lanthanide oxides have practical potential as alternative additives to develop Ni-based ODS alloys, among which La2O3 would be the most advantageous in improving the mechanical properties.
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•Four types of Ni-based ODS alloys with the addition of Y2O3, CeO2, La2O3, and Gd2O3 were manufactured via MA and HIP.•Milled powders with the addition of La2O3 had a better microstructure stability at elevated temperatures.•Lanthanide oxides had better contribution to strengthening the mechanical properties of Ni-based ODS alloys than Y2O3.•La2O3 was the most advantageous in refining both oxide particles and grain size.
Solutal convection in porous media is thought to be controlled by the molecular Rayleigh number, Ram, the ratio of the buoyant driving force over diffusive dissipation. The mass flux should increase ...linearly with Ram and the finger spacing should decrease as
Ram−1/2. Instead, our experiments find that flux levels off at large Ram and finger spacing increases with Ram. Here we show that the convective pattern is controlled by a dispersive Rayleigh number, Rad, balancing buoyancy and dispersion. Increasing the bead size of the porous medium increases Ram but decreases Rad and hence coarsens the pattern. While the flux is predominantly controlled by Ram, the anisotropy of mechanical dispersion leads to an asymmetry in the pattern that limits the flux at large bead sizes.
Plain Language Summary
Pattern formation in simple physical systems is intriguing, and convection in porous media is an example that was thought to be well understood. Convection is controlled by the balance between buoyant driving forces and dissipative mechanisms such as diffusion that smear out the concentration and hence density differences. Here we use simple laboratory experiments to show that the convective pattern is controlled by a different process than previously thought. A Rayleigh number based on mechanical dispersion, which is independent of fluid properties, predicts the flow pattern of solutal convection in bead packs.
Key Points
The classic Rayleigh number predicts neither flux nor pattern of convection in bead packs
The convective pattern is controlled by mechanical dispersion and independent of fluid properties
The anisotropy of dispersion breaks the symmetry of the convective pattern and reduces the flux
This study aimed to examine the effects of the parameters, the group velocity dispersion (GVD), the third-order dispersion (3OD), spatio-temporal dispersion (STD), the third-order spatio-temporal ...dispersion (TO-STD), self-steepening effect and nonlinear dispersions terms, included in the equations modeling optical fiber phenomena on the optical solitons. Therefore, the aim of this study is not to apply a method to derive different types of optical soliton solutions and their graphical representation. For this purpose, the Biswas–Arshed equation, which is the important equation to model to compensate for the effects of dispersion in the case of nonlinearity and to provide the necessary balance for soliton propagation to continue, is investigated by utilizing the new Kudryashov approach.
First, the nonlinear ordinary differential (ODE) form of the investigated problem was obtained by using the complex wave transform. Afterward, a linear equation system was created using the new Kudryashov scheme, the appropriate solution sets were found, and the bell-shaped soliton, which was desired to be examined, was obtained by using these sets and selecting the suitable parameter values. Depending on the definition of the Biswas–Arshed equation and the constraints of the method, two-dimensional graphical views were obtained, and necessary comments were made to better observe the effect by giving different values to the parameters whose effect is desired to be examined.
At the end of the detailed examination, important and fundamental findings were obtained. Such that the model parameters do not only consist of a set of numerical values that can be given to obtain various soliton graphics, but also, the model parameters have a crucial effect on the soliton behavior. In addition, parameter values should be determined by evaluating both each other and the physical design of the investigated equation.
This study, which examines the effects of model parameters on the behavior of the Biswas–Arshed equation on the behavior of bell shape soliton, is presented for the first time in this article.
One of the most powerful tools to assess the dynamical characteristics of time series is entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long signals. Permutation ...entropy (PE), as a broadly used irregularity indicator, considers only the order of the amplitude values and hence some information regarding the amplitudes may be discarded. To tackle these problems, we introduce a new method, termed dispersion entropy (DE), to quantify the regularity of time series. We gain insight into the dependency of DE on several straightforward signal-processing concepts via a set of synthetic time series. The results show that DE, unlike PE, can detect the noise bandwidth and simultaneous frequency and amplitude change. We also employ DE to three publicly available real datasets. The simulations on real-valued signals show that the DE method considerably outperforms PE to discriminate different groups of each dataset. In addition, the computation time of DE is significantly less than that of SE and PE.
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