Interface Method Research Articles

A discontinuity capturing shallow neural network for elliptic interface problems

In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN) for approximating d-dimensional piecewise continuous functions and for solving elliptic interface problems is developed. There are three novel features in the present network; namely, (i) jump discontinuities are accurately captured, (ii) it is completely shallow, comprising only one hidden layer, (iii) it is completely mesh-free for solving partial differential equations. The crucial idea here is that a d-dimensional piecewise continuous function can be extended to a continuous function defined in (d+1)-dimensional space, where the augmented coordinate variable labels the pieces of each sub-domain. We then construct a shallow neural network to express this new function. Since only one hidden layer is employed, the number of training parameters (weights and biases) scales linearly with the dimension and the neurons used in the hidden layer. For solving elliptic interface problems, the network is trained by minimizing the mean square error loss that consists of the residual of the governing equation, boundary condition, and the interface jump conditions. We perform a series of numerical tests to demonstrate the accuracy of the present network. Our DCSNN model is efficient due to only a moderate number of parameters needed to be trained (a few hundred parameters used throughout all numerical examples), and the results indicate good accuracy. Compared with the results obtained by the traditional grid-based immersed interface method (IIM), which is designed particularly for elliptic interface problems, our network model shows a better accuracy than IIM. We conclude by solving a six-dimensional problem to demonstrate the capability of the present network for high-dimensional applications.

Facile low temperature ammonolysis synthesis of CeO2-xNx for enhanced photoelectrochemical H2 generation

A facile solid–gas interface method for anionic substitution of nitrogen in CeO2 lattice is reported. In this study, a low temperature ammonolysis method is developed as an alternate to high temperature ammonolysis of CeO2. Cerium nitrate precursor is thermally treated directly by ammonia gas at 550 °C to prepare cerium-oxy-nitride samples which are tested for photoelectrochemical hydrogen evolution activity (HER). The CeO2-xNx material synthesized by low temperature ammonolysis of cerium nitrate shows lower onset potential ηlight = -1.04 V vs Ag/AgCl under light, large photocurrent density of 0.2 µA/cm2 at −0.4 V applied potential. The proposed method of ammonolysis of salt precursors is an excellent method for the synthesis of oxy-nitrides at low temperatures without residual carbon impurities for higher photocatalytic efficiencies.

A series of multi-domain matched interface and boundary algorithms for dynamic and static responses of annular sectorial plates

A series of multi-domain matched interface and boundary (MDMIB) algorithms are proposed to analyze the free vibration and bending of annular sectorial thin plates. Through an in-depth understanding of the matched interface and boundary (MIB) method, six types of MDMIB algorithms are developed herein by using various domain decomposition techniques for handling bi-directional crossing interfaces due to non-homogeneous elastic foundations and discontinuous loads, which cannot be treated by the conventional MIB method. The rationale of the MDMIB method is to divide the entire computational domain into multiple subdomains that can be connected by using the interconnecting conditions between crossing interfaces and mesh lines. Annular sectorial plates, having non-uniform boundary conditions, partially supported elastic foundations, and patch loads, are all considered. Numerous examples are chosen to examine the computational performance of the MDMIB algorithms. For verification, the present results are checked against existing and finite element solutions. It is shown that the proposed MDMIB algorithms are highly effective to deal with the bi-directional interface problems.

Development of an Electrooculogram (EOG) and Surface Electromyogram (sEMG)-Based Human Computer Interface (HCI) Using a Bone Conduction Headphone Integrated Bio-Signal Acquisition System

Human–computer interface (HCI) methods based on the electrooculogram (EOG) signals generated from eye movement have been continuously studied because they can transmit the commands to a computer or machine without using both arms. However, usability and appearance are the big obstacles to practical applications since conventional EOG-based HCI methods require skin electrodes outside the eye near the lateral and medial canthus. To solve these problems, in this paper, we report development of an HCI method that can simultaneously acquire EOG and surface-electromyogram (sEMG) signals through electrodes integrated into bone conduction headphones and transmit the commands through the horizontal eye movements and various biting movements. The developed system can classify the position of the eyes by dividing the 80-degree range (from −40 degrees to the left to +40 degrees to the right) into 20-degree sections and can also recognize the three biting movements based on the bio-signals obtained from the three electrodes, so a total of 11 commands can be delivered to a computer or machine. The experimental results showed the interface has accuracy of 92.04% and 96.10% for EOG signal-based commands and sEMG signal-based commands, respectively. As for the results of virtual keyboard interface application, the accuracy was 97.19%, the precision was 90.51%, and the typing speed was 5.75–18.97 letters/min. The proposed interface system can be applied to various HCI and HMI fields as well as virtual keyboard applications.

A Modified Algorithm Based on Haar Wavelets for the Numerical Simulation of Interface Models

In this paper, a new numerical technique is proposed for the simulations of advection-diffusion-reaction type elliptic and parabolic interface models. The proposed technique comprises of the Haar wavelet collocation method and the finite difference method. In this technique, the spatial derivative is approximated by truncated Haar wavelet series, while for temporal derivative, the finite difference formula is used. The diffusion coefficients, advection coefficients, and reaction coefficients are considered discontinuously across the fixed interface. The newly established numerical technique is applied to both linear and nonlinear benchmark interface models. In the case of linear interface models, Gauss elimination method is used, whereas for nonlinear interface models, the nonlinearity is removed by using the quasi-Newton linearization technique. TheL∞errors are calculated for different number of collocation points. The obtained numerical results are compared with the immersed interface method. The stability and convergence of the method are also discussed. On the whole, the numerical results show more efficiency, better accuracy, and simpler applicability of the newly developed numerical technique compared to the existing methods in literature.

Full-Scale Simulation of the Fluid–Particle Interaction Under Magnetic Field Based on IIM–IBM–LBM Coupling Method

In this article, a full-scale computational model for fluid–particle interaction under a magnetic field is developed. In this model, the fluid field is solved by the lattice Boltzmann method, and the hydrodynamic force acting on the particle is computed by the immersed boundary method . The numerical solutions of the magnetic field in the fluid–solid domain are achieved by the immersed interface method with a finite difference scheme, in which the normal and tangential jump conditions of the magnetic field intensity are applied to modify the standard finite difference scheme. The magnetic stress tensor along the fluid–particle interface can be calculated accurately. Unlike the widely used point–dipole model, the magnetic force acting on the particle is determined by the stress integration method. Numerical simulation of several numerical tests are carried out to validate the proposed model. The numerical results demonstrate the validity of the present model. Moreover, the magnetoviscous effect is studied by simulating the motion of elliptical particles under the uniform magnetic field in shear flow.

Adoption of serious games by teachers: the analysis method of structure, interface and use

In this article, we determine how to facilitate the analysis of serious games so that teachers could effectively integrate them in their teaching. The aim is to identify the mechanisms that would make serious games exploitation useful. We propose a method for the analysis of serious games that is based on the separation of their components along three phases. In addition, we set up a platform based on a data analysis process that is composed of six steps that help to set the basis of a verification procedure that targets the content of a game and thus facilitates the work of teachers through effective implementation of serious games as teaching strategies (TIC). The obtained experimental results show that 82.5% of the study participants expressed that the use of the platform has helped them to change their perspective on the need to use serious games as an educational tool.

Optimization method for a radar situation interface from error-cognition to information feature mapping

With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design information. We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.

Large eddy simulations of reacting and non-reacting transcritical fuel sprays using multiphase thermodynamics

We present a novel framework for high-fidelity simulations of inert and reacting sprays at transcritical conditions with highly accurate and computationally efficient models for complex real-gas effects in high-pressure environments, especially for the hybrid subcritical/supercritical mode of evaporation during the mixing of fuel and oxidizer. The high-pressure jet disintegration is modeled using a diffuse interface method with multiphase thermodynamics, which combines multi-component real-fluid volumetric and caloric state equations with vapor–liquid equilibrium calculations for the computation of thermodynamic properties of mixtures at transcritical pressures. Combustion source terms are evaluated using a finite-rate chemistry model, including real-gas effects based on the fugacity of the species in the mixture. The adaptive local deconvolution method is used as a physically consistent turbulence model for large eddy simulation (LES). The proposed method represents multiphase turbulent fluid flows at transcritical pressures without relying on any semi-empirical breakup and evaporation models. All multiphase thermodynamic model equations are presented for general cubic state equations coupled with a rapid phase-equilibrium calculation method that is formulated in a reduced space based on the molar specific volume function. LES results show a very good agreement with available experimental data for the reacting and non-reacting engine combustion network benchmark spray A at transcritical operating conditions.

3D Assembly Model Retrieval Method for a Multiassembly Interface

3D model retrieval is increasingly becoming a hot topic in today’s research. The existing model retrieval methods are limited to the retrieval between similar models, while the matching retrieval for models with assembly relationships is neglected. They cannot realize the prediction of the virtual assembly and make the assemblers to spend extensive time detecting when there are assembly errors in the model. This work proposes a 3D assembly model retrieval method for multiassembly interface, which can retrieve the parts or subassemblies that can be assembled with the input model. In this work, a method for the reuse of the 3D assembly mode is also provided. The method here involves a series of steps, which includes an attribute adjacency graph to express the 3D assembly models, followed by the definition of a conjugated subgraph, and the serialization of the vertices of the graph for the Atlas of the assembly model. Finally, the frequent subgraph mining method is improved, and the 3D assembly model that satisfies the multiassembly interface is extracted. The article claims through the obtained results that this method not only allows the performance of the 3D assembly model retrieval of multiassembly interfaces but also the extraction of the 3D model required by designers, improving, at the same time, the product design efficiency.

Smoothed boundary method for simulating incompressible flow in complex geometries

Simulating flow through porous media with explicit considerations of complex microstructures is very challenging using conventional sharp-interface methods because of the difficulties in generating meshes conformal to complex geometries. In this work, a diffuse interface embedded boundary method known as the Smoothed Boundary Method (SBM) is utilized to facilitate simulations of fluid dynamics involving complex geometries. In diffuse-interface methods, the geometry is described by a domain parameter. The SBM allows the straightforward reformulation of the time-dependent Navier–Stokes equations in terms of this domain parameter, using only differentiation identities. Thus, enforcing the appropriate boundary conditions at the irregular embedded boundary is greatly simplified. Adaptive mesh refinement is used to increase the accuracy of the diffuse interface method by allowing a thinner interfacial thickness to be used in the domain parameter. The SBM-formulated Navier–Stokes equations are solved with the Finite Difference Method on refined mesh systems. Sharp-interface Finite Element Method simulations using the commercial software COMSOL on body-conforming meshes are also provided for comparison. Favorable agreement between the two methods is observed. Since it is no longer necessary for the mesh to conform to the complex geometry, the grid system for the SBM simulations can be generated rapidly and without additional manual interventions, making the entire simulation process more expedient.

Comparison of evolving interfaces, triple points, and quadruple points for discrete and diffuse interface methods

The evolution of interfaces is intrinsic to many physical processes ranging from cavitation in fluids to recrystallization in solids. Computational modeling of interface motion entails a number of challenges, many of which are related to the range of topological transitions that can occur over the course of the simulation. Microstructure evolution in a polycrystalline material that involves grain boundary motion is a particularly complex example due to the extreme variety, heterogeneity, and anisotropy of grain boundary properties. Accurately modeling this process is essential to determining processing-structure–property relationships in polycrystalline materials though. Simulations of microstructure evolution in such materials often use diffuse interface methods like the phase field method that are advantageous for their versatility and ease of handling complex geometries but can be prohibitively expensive due to the need for high interface resolution. Discrete interface methods require fewer grid points and can consequently exhibit better performance but have received comparatively little attention, perhaps due to the difficulties of maintaining the mesh and consistently implementing topological transitions on the grain boundary network. This work explicitly compares a recently-developed discrete interface method to a multiphase field method on several classical problems relating to microstructure evolution in polycrystalline materials: a shrinking spherical grain, the steady-state triple junction dihedral angle, and the steady-state quadruple point dihedral angle. In each case, the discrete method is found to meet or outperform the multiphase field method with respect to accuracy for comparable levels of refinement, demonstrating its potential efficacy as a numerical approach for microstructure evolution in polycrystalline materials.

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freestyle character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a–z) and digits (0–9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.

A fourth-order compact implicit immersed interface method for 2D Poisson interface problems

This paper presents a fourth-order compact immersed interface method to solve two-dimensional Poisson equations with discontinuous solutions on arbitrary domains divided by an interface. The compact scheme only employs a nine-point stencil for each grid point on the computational domain. The new approach is based on an implicit formulation obtained from generalized Taylor series expansions, and it is constructed from a few modifications to the central finite difference near the interface. The discretization results in a linear system in which the matrix coefficients are the same as the ones for smooth solutions, and the right-hand side system is modified by adding terms known as jump contributions. These contributions are only calculated at those points where the nine-point stencil cuts the interface. However, the contribution formulas require the knowledge of Cartesian jumps up to fourth-order. In this paper, we derived them using only the principal jump conditions and the jumps coming from the known right-hand function of the Poisson equation. We present numerical experiments in two dimensions to verify the feasibility and accuracy of the proposed method. Thus, the implicit immersed interface method results in an attractive fourth-order compact scheme that is easy to be implemented and applied to arbitrary interface shapes.

SiO2 Microsphere Array Coated by Ag Nanoparticles as Raman Enhancement Sensor with High Sensitivity and High Stability.

In this paper, a monolayer SiO2 microsphere (MS) array was self-assembled on a silicon substrate, and monolayer dense silver nanoparticles (AgNPs) with different particle sizes were transferred onto the single-layer SiO2 MS array using a liquid–liquid interface method. A double monolayer “Ag@SiO2” with high sensitivity and high uniformity was prepared as a surface-enhanced Raman scattering (SERS) substrate. The electromagnetic distribution on the Ag@SiO2 substrate was analyzed using the Lumerical FDTD (finite difference time domain) Solutions software and the corresponding theoretical enhancement factors were calculated. The experimental results show that a Ag@SiO2 sample with a AgNPs diameter of 30 nm has the maximal electric field value at the AgNPs gap. The limit of detection (LOD) is 10−16 mol/L for Rhodamine 6G (R6G) analytes and the analytical enhancement factor (AEF) can reach ~2.3 × 1013. Our sample also shows high uniformity, with the calculated relative standard deviation (RSD) of ~5.78%.

A second-order extension of a robust implicit–explicit acoustic-transport splitting scheme for two-phase flows

Diffuse interface methods have proven their ability to simulate complex two-phase flows. A number of robust numerical schemes have been developed to simulate such flows involving large density and pressure ratios. Diffusion induced by these methods, however, makes it difficult to localize the interface between the two fluids. To overcome this issue, while retaining the advantages of diffuse interface methods, a second-order extension using a Monotonic Upstream-centered Scheme for Conservation Laws-type (MUSCL-type) method of the implicit–explicit acoustic-transport splitting scheme introduced in Peluchon et al. (2017) is presented. A specific compressive limiter is used for the volume fraction in order to limit the diffusion of the interface between the two fluids.Numerical simulations are presented to illustrate the capability of the proposed new method to simulate highly complex compressible two-phase flows.

Flower-like Composite Material Delivery of Co-Packaged Lenvatinib and Bufalin Prevents the Migration and Invasion of Cholangiocarcinoma.

The co-delivery of multiple drugs using nanocarriers has been recognized as a promising strategy for cancer treatment to enhance therapeutic efficacy. In this study, a monodisperse mesoporous silica nanoparticle (mSiO2) is prepared and functionalized into high-efficiency loaded Lenvatinib and Bufalin for targeted delivery to Cholangiocarcinoma (CCA). mSiO2 was synthesized on solid silica nanoparticles by oil–water interface method, and highly monodisperse mSiO2 with uniform morphology was obtained. mSiO2 was then sequentially modified by polyethylene glycol (PEG) and the targeting molecule folic acid (FA). mSiO2-FA was designed as co-delivery system for Lenvatinib (Le) and Bufalin (Bu) to increase drug availability and highly target tumor cells. Compared with unfunctionalized mSiO2, mSiO2-FA can more efficiently enter human CCA cell lines (9810 cells) and enhance intracellular drug delivery. Moreover, drug-loaded mSiO2-FA (Le/Bu@mSiO2-FA) significantly inhibited the viability, migration and invasion of 9810 cells. In vivo, the nanocomplex significantly reduced the tumor load in CCA tumor-bearing mouse models compared to Le or Bu alone. The current work provides a useful strategy for highly targeted and multidrug-resistance reversal therapy for CCA.

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.

The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs “Mapping Knowledge Domains” (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.

P120 regulates E-cadherin expression in nasal epithelial cells in chronic rhinosinusitis.

The epithelial barrier plays an important role in the regulation of immune homeostasis. The effect of the immune environment on E-cadherin has been demonstrated in previous studies. This discovery prompted new research on the targeting mechanism of E-cadherin in chronic rhinosinusitis (CRS). E-cadherin and p120 expression was determined by quantitative RT-PCR, and western blot. The interaction between E-cadherin and p120 was assessed by immunofluorescence staining and coimmunoprecipitation assays. Human nasal epithelial cells (HNECs) were cultured with submerged methods and transfected with p120-specific small interfering RNA. In other experiments, HNECs differentiated with the air-liquid interface (ALI) method were stimulated with various cytokines and Toll-like receptor (TLR) agonists. The barrier properties of differentiated HNECs were determined by assessing fluorescent dextran permeability. E-cadherin and p120 expression was decreased in HNECs from patients with CRS, and the p120 protein expression level was positively correlated with that of E-cadherin. Two isoforms of p120 (p120-1 and p120-3) were expressed in HNECs, with p120-3 being the main isoform. Knocking down p120 in HNECs cultured under submerged conditions significantly reduced the E-cadherin protein expression. The Rac1 inhibitor NSC23766 reversed the protein expression of E-cadherin in p120 knockdown experiments. Inflammatory mediators, including IL-4, TNF-α, TGF- β, LPS and IFN-Î, reduced E-cadherin and p120 protein expression and increased paracellular permeability. Dexamethasone abolished the downregulation of E-cadherin and p120 caused by inflammatory mediators. p120 is involved in regulating E-cadherin protein expression in CRS. Dexamethasone may alleviate the reduction in E-cadherin and p120 protein expression caused by inflammatory mediators.

A diffuse interface method for solid-phase modeling of regression behavior in solid composite propellants

Solid composite propellants (SCPs) are ubiquitous in the field of propulsion. In order to design and control solid rocket motors, it is critical to understand and accurately predict SCP regression. Regression of the burn surface is a complex process resulting from thermo-chemical-mechanical interactions, often exhibiting extreme morphological changes and topological transitions. Diffuse interface methods, such as phase field (PF), are well-suited for modeling processes of this type, and offer some distinct numerical advantages over their sharp-interface counterparts. They also provide a convenient framework for incorporating multiple multiphysical dynamics. In this work, we present a phase-field method for modeling the regression of SCPs with varying species and geometry. We construct the model from a thermodynamic perspective, leaving the base formulation general. A diffuse-species-interface field is employed as a mechanism for capturing complex burn chemistry in a reduced-order fashion, making it possible to model regression from the solid phase only. The computational implementation, which uses block-structured adaptive mesh refinement and temporal substepping for increased performance, is briefly discussed. The model is then applied to four test cases: (i) pure AP monopropellant, (ii) AP PBAN sandwich, (iii) AP HTPB sandwich, and (iv) spherical AP particles packed in HTPB matrix in two and three dimensions. In all cases, reasonable quantitative agreement is observed, even when the model is applied predictively (i.e., no parameter adjustment), as in the case of (iv). The validation of the proposed PF model demonstrates its efficacy as a numerical design tool for future SCP investigation.