Interface Method Research Articles

Reconstruction of electromagnetic parameters of the scattering from overfilled cavities using a complex-valued convolutional neural network

We reconstruct the electromagnetic parameters of the scattering from overfilled cavities by developing a complex-valued convolutional neural network (CV-CNN) method. Since the scattering field and electromagnetic parameters are usually complex values, the input, output and weight parameters of this method are set to complex values in order to capture the characteristics of the electromagnetic field and parameters more accurately. The complex-valued matrix of the input layer is composed of the scattering field intensity received by the receivers outside the overfilled cavity, which is solved by the Petrov–Galerkin finite element interface method based on uniform non-body-fitted meshes. By means of the received scattering field intensity outside the overfilled cavities, the proposed CV-CNN can accurately reconstruct the electromagnetic parameters of arbitrarily shaped overfilled cavities with homogeneous, inhomogeneous and anisotropic media. The experimental results show that the reconstruction accuracy of CV-CNN is significantly higher than real-valued convolutional neural network (RV-CNN) with the same architecture. Moreover, the appropriate optimizer and filter size have a positive influence on the reconstruction effects. Our study can provide a new development in the electromagnetic parameter reconstruction of the cavity scattering.

Inherent Thermal-Noise Problem in Addressing Qubits

Qubit-specific measurement in a superconducting quantum processor requires physical interconnects that traverse 4 orders of magnitude in temperature from 293 K to 10 mK. Although the quantum processor can be thermalized and shielded from electromagnetic noise, the interconnects themselves introduce an unavoidable remote heat bath that causes decoherence of quantum states. In the present work, we report quantitative and device-independent measurements of the power radiated to the quantum processor from its control lines. Our results have been obtained using a calibrated bolometer that operates within a millikelvin environment with time-resolved measurement capability. In the limit of zero applied power, the noise power emitted to the quantum processor is equivalent to that of a blackbody with temperature 63–71 mK for the prototypical drive lines in the study. Experimentally, we increase the applied power of a simulated control signal to map out the resulting temperature rise and thermal time constant of five prototypical drive-line varieties. We input the data to an open quantum system model to demonstrate the trade-off between dissipated signal power, transmon-qubit lifetime, pure dephasing, gate fidelity, and the implied decoherence rates due to self-heating during microwave operations. Beyond explaining dephasing rates observed in the literature, our work sets the stage for accurate noise modeling in novel quantum computer interfacing methods due to our device-agnostic approach. Published by the American Physical Society 2024

Research on the Design of Telemedicine Interface for the Elderly based on the Double Diamond Model

It has become an inevitable trend for China to enter an aging society. The rapid growth of the elderly population has placed a huge burden on the existing medical system. Existing telemedicine APPs do not take into account the characteristics and preferences of the elderly. In order to help the elderly better accept and use the telemedicine system and improve user satisfaction. It is proposed to optimize the telemedicine interface design based on the double diamond model, and the design process around the four stages of exploration, definition, development and interaction is explained in detail. Through semi-structured interviews with the elderly and designers, the user needs of the elderly for telemedicine and the design elements, tools and methods of the telemedicine interface for the elderly were obtained. Draw a user experience journey map and conduct telemedicine interface design practice for the elderly. Improve the elderly’s experience of using telemedicine, improve the comfort performance of products, and promote the development of the elderly health industry.

Numerical study of the drag force, interfacial area and mass transfer in bubbles in a vertical pipe

A systematic numerical study of drag force and mass transfer in gravity-driven bubbles in a vertical pipe is performed at Re∼O(100−1000). This research employs a parallel multi-marker unstructured conservative level set method for interface capturing, avoiding the numerical coalescence in bubble swarms. The finite volume method discretizes transport equations on 3D collocated unstructured meshes. Unstructured flux limiter schemes solve the convective term of transport equations, preserving the numerical stability at high Reynolds numbers and high-density ratios. Thermodynamic equilibrium is assumed at the interface for the concentration of chemical species. The hydrodynamics and mass transfer of bubbles are validated against classical correlations from the literature. Finally, direct simulations are executed to develop new correlations for the drag force, normalized bubble surface and Sherwood number for bubbles in a vertical pipe.

A Compact Coupling Interface Method with Second-Order Gradient Approximation for Elliptic Interface Problems

A Compact Coupling Interface Method with Second-Order Gradient Approximation for Elliptic Interface Problems

Analytical solution for horizontal vibration of partially embedded pipe pile group in layered saturated soil

AbstractBased on Biot's dynamic wave theory and Novak's plane strain model (PSM), an analytical model for the horizontal vibration of a partially embedded pipe pile group (PEPPG) in layered saturated soil (LSS) that considers the soil‐plugging effect was established: First, the frequency‐domain analytical expressions of the saturated surrounding soil (SSS) and soil plug were obtained by introducing operator decomposition theory and the variable separation method. Second, the analytical solution of the horizontal impedance of a partially embedded pipe pile in the LSS is derived using the coupled condition of the pile–soil interface and transfer matrix method. Finally, an analytical solution for the horizontal impedance of PEPPG in LSS was derived by introducing the horizontal dynamic interaction factor of piles and the superposition method. The derived solution is then reduced and compared with existing theoretical solutions to verify its rationality. In addition, further parametric analysis was conducted to investigate the influences of pile spacing, embedment ratio, properties of layered SSS, and soil‐plugging on the horizontal vibration characteristics (HVCs) of PEPPG.

Research on Six-Degree-of-Freedom Refueling Robotic Arm Positioning and Docking Based on RGB-D Visual Guidance

The main contribution of this paper is the proposal of a six-degree-of-freedom (6-DoF) refueling robotic arm positioning and docking technology guided by RGB-D camera visual guidance, as well as conducting in-depth research and experimental validation on the technology. We have integrated the YOLOv8 algorithm with the Perspective-n-Point (PnP) algorithm to achieve precise detection and pose estimation of the target refueling interface. The focus is on resolving the recognition and positioning challenges of a specialized refueling interface by the 6-DoF robotic arm during the automated refueling process. To capture the unique characteristics of the refueling interface, we developed a dedicated dataset for the specialized refueling connectors, ensuring the YOLO algorithm’s accurate identification of the target interfaces. Subsequently, the detected interface information is converted into precise 6-DoF pose data using the PnP algorithm. These data are used to determine the desired end-effector pose of the robotic arm. The robotic arm’s movements are controlled through a trajectory planning algorithm to complete the refueling gun docking process. An experimental setup was established in the laboratory to validate the accuracy of the visual recognition and the applicability of the robotic arm’s docking posture. The experimental results demonstrate that under general lighting conditions, the recognition accuracy of this docking interface method meets the docking requirements. Compared to traditional vision-guided methods based on OpenCV, this visual guidance algorithm exhibits better adaptability and effectively provides pose information for the robotic arm.

Numerical modelling of large elasto-plastic multi-material deformations on Eulerian grids

Abstract The paper deals with an Eulerian model for heterogeneous multiphase (multi-material) elasto-plastic media based on the diffuse interface method in the one-dimensional uniaxial strain approximation. In this approximation, an equilibrium hypoelastic Wilkins bi-material model is derived. This is carried out on the basis of an analogy between the elasto-plastic Wilkins and hydrodynamic Euler models. For the obtained model, a Godunov-type numerical method is developed based on the approximate Riemann solver HLLC. The results of the proposed Eulerian diffuse interface model are compared with reference solutions on moving grids with explicit tracking interfaces. It is shown that the present diffuse interface numerical model describes with good accuracy strong shock-wave processes in heterogeneous multiphase elasto-plastic media.

Hydrophobic and zincophilic organic hierarchical nano-membranes with ordered molecular packing for stable zinc metal anodes

Rechargeable aqueous Zn metal batteries hold significant potentials as one of the next-generation energy-storage devices due to their low cost and high safety. However, the uncontrollable dendrite growth and low efficiency in plating/stripping of Zn anodes hinder the commercial application of aqueous Zn-ion batteries (AZIBs). Herein, inspired by hydrophobic lotus, a multifunctional organic hierarchical nano-membrane serving as an artificial protective layer is constructed through the gas-liquid interface method, using self-assembled organic nanosheets (NOS). The organic nano-membrane exhibits ordered molecular packing, accompanied by uniform distribution of zincophilic carbonyl (CO) groups on the Zn metal surface. These groups function as active sites, homogenizing Zn2+ flux and accelerating Zn2+ deposition. Simultaneously, the hydrophobic membrane isolates active water, thereby restraining side reactions, effectively suppressing corrosion and extremely inhibiting dendrite growth. Consequently, NOS@Zn anode exhibits stable cycling over 2400 h and an average coulombic efficiency (CE) of 99.69 % (600 cycles) at 1 mA cm−2. Furthermore, NOS@Zn||CNT-MnO2 full cell presents remarkable capacity retention, approximately 5 times higher than that of Zn||CNT-MnO2 after 3000 cycles at 2 A g−1. This work develops an innovative organic interface modification material paving the way for dendrite-free Zn metal anodes and advancing the practical application of large-scale Zn-based energy storage systems.

A MAC grid based FFT-AMIB solver for incompressible Stokes flows with interfaces and singular forces

In this paper, a second order accurate Augmented Matched Interface and Boundary (AMIB) method is introduced for solving the incompressible Stokes flows with interfaces and singular forces. Based on the MAC staggered grid for velocity and pressure, the zeroth order and first order jump conditions are repeatedly enforced in the AMIB scheme to generate fictitious values around the interface, which allow an accurate approximation of the Cartesian derivative jumps. By treating all jump quantities as auxiliary variables and coupling them with the corrected finite difference formulas, the Stokes equations can be discretized into an augmented system. The proposed FFT-AMIB solver does not need to take the pressure boundary condition into account. Moreover, in the Schur complement solution of the augmented system, the discrete Laplacian can be efficiently inverted by using the fast Fourier transform (FFT). Numerical results show that the FFT-AMIB solver is very efficient and can achieve second order accuracy for both velocity and pressure. The iteration number of Bi-CGSTAB in solving the resulting augmented system only weakly depends on the mesh size. When the interface is allowed to be moved under the surface tension, the proposed algorithm can maintain the divergence-free condition and area conservation very well.

Weak Galerkin methods for elliptic interface problems on curved polygonal partitions

This paper presents a new weak Galerkin (WG) method for elliptic interface problems on general curved polygonal partitions. The method’s key innovation lies in its ability to transform the complex interface jump condition into a more manageable Dirichlet boundary condition, simplifying the theoretical analysis significantly. The numerical scheme is designed by using locally constructed weak gradient on the curved polygonal partitions. We establish error estimates of optimal order for the numerical approximation in both discrete H1 and L2 norms. Additionally, we present various numerical results that serve to illustrate the robust numerical performance of the proposed WG interface method.

Gossypium hirsutum calmodulin-like protein (CML 11) interaction with geminivirus encoded protein using bioinformatics and molecular techniques

Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.

Motion of a small bubble in forced vibrating sessile drop

In this letter, the motion of small gas bubbles within sessile water drops on a vibrating substrate is investigated numerically using a two-phase diffuse interface method. Depending on the amplitude of the plate vibration, the motion of the gas bubbles falls into three distinct regimes: oscillating within the drop, sticking to the substrate, or escaping from the drop. In particular, the motion of oscillating bubbles follows a harmonic function, and is found to be closely related to a combined effect of the deformation of the sessile drop and the vibration of the plate. To interpret the underlying mechanism, we analyze the dominant forces acting on the bubbles in the non-inertial framework fixed to the plate, and take account of the periodic deformation of the drop, which effectively induces flow acceleration inside the drop. As a result, we establish a theoretical model to predict the bubble motion, and correlate the amplitude and phase difference of the bubble with the Bond and Strouhal numbers. The theoretical prediction agrees with our numerical results. These findings and theoretical analysis provide new insights into controlling bubble motion in sessile drops.

Experimental research on shear resistance performance of fabricated reinforced concrete layered beams

The construction of prefabricated beams includes two steps: prefabrication and cast in-situ. However, for the bearing beam, the shear stress on the joint surface of these two parts may be large. This is detrimental to the shear resistances of layered beams. To evaluate the shear resistance performances of such layered beams, three different influencing factors—the ratio of the precast layer height to the beam height, shear-span to depth ratio, and the treatment method of new and old concrete interface—were considered, and shear resistance tests of 9 groups of beams were carried out. The failure mode, carrying capacity, deflection, and strain value of each test beam were analyzed. The results show that the shear bearing capacity of layered beams decreases by 34.12% with the increase of shear-span to depth ratio, and with the increase of the ratio of precast layer height to beam height, the shear bearing capacity of layered beams decreases by 14.17%. The shear bearing capacity of the layered beam with steel bars inserted in the vertical direction of the new and old concrete interface is about 8.43% higher than that of the layered beam without steel bars. According to the test results, the formula for calculating the shear bearing capacity of layered beams considering the layered height ratio and shear-span to depth ratio was proposed. At the same time, the interface resistant shear design method concerning reinforced concrete layered beams is proposed.

Garnet-based solid state batteries benefitting from an ionic/electronic mixed conductive interface constructed by lithiation of porous FeS2

Garnet-based solid-state lithium metal batteries are considered as the potential candidates for the next-generation energy storage systems due to their high energy density, wide operating temperature, and high safety. However, the poor wettability of the lithium metal anode/garnet interface, the large interface resistance, and the risk of lithium dendrites growing and even penetrating electrolytes during cycling limit the practical application of garnet-based solid-state lithium metal batteries. In this work, a porous network FeS2 with an amorphized structure is prepared by using the solvothermal method and used as the Li/garnet interface modification layer. The porous FeS2 can be in situ converted into a Li2S/Fe mixed conductive layer by the thermal lithiation of molten metallic lithium. This mixed conductive layer can significantly reduce the interface resistance, ensure the close contact between Li and garnet, and inhibit the growth of lithium dendrites. The interface resistance of the modified Li/FeS2-LLZTO (LLZTO is Li6.5La3Zr1.5Ta0.5O12) interface at 60 °C is as small as 15.20 Ω cm2. The ionic conductivity of fully lithiated FeS2 is estimated to be 1.58 × 10−6 S cm−1 at room temperature. The Li/FeS2-LLZTO/Li symmetrical cell can cycle stably for more than 400 h at a high current density of 400 μA cm−2, with the voltage polarization of only about 25 mV, and can withstand a larger current density of 600 μA cm−2 without the polarization exceeding 50 mV. These results demonstrate the feasibility of in situ lithiation of porous iron sulfide into a mixed ion/electron conductive layer as a solid-state garnet interface modification strategy and provide the new interface method for the development of high-performance solid-state lithium metal batteries.

A Time-Domain Wavenumber Integration Model for Underwater Acoustics Based on the High-Order Finite Difference Method

Simulating the acoustic field excited by pulse sound sources holds significant practical value in computational ocean acoustics. Two primary methods exist for modeling underwater acoustic propagation in the time domain: the Fourier synthesis technique based on frequency decomposition and the time-domain underwater acoustic propagation model (TD-UAPM). TD-UAPMs solve the wave equation in the time domain without requiring frequency decomposition, providing a more intuitive explanation of the physical process of sound energy propagation over time. However, time-stepping numerical methods can accumulate numerical errors, making it crucial to improve the algorithm’s accuracy for TD-UAPMs. Herein, the time-domain wavenumber integration model SPARC was improved by replacing the second-order finite element method (FEM) with the high-order accuracy finite difference method (FDM). Furthermore, the matched interface and boundary (MIB) method was used to process the seabed more accurately. The improved model was validated using three classic underwater acoustic benchmarks. By comparing the acoustic solutions obtained using the FDM and the FEM, it is evident that the improved model requires fewer grid points while maintaining the same level of accuracy, leading to lower computational costs and faster processing compared to the original model.

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

Simulation of Hydrate Migration and Deposition in Pipe with Diameter Reduction and Direction Variation

Summary Pipes with diameter reduction and direction variation are very common in deepwater extraction. While the high-pressure and low-temperature conditions may trigger severe hydrate problems, current studies on hydrate particle migration and deposition are mainly carried out in pipes with a constant diameter, whereas the law of diameter reduction has been less explored; in particular, the effect of diameter reduction + direction variation in pipe has not been reported. In this study, a model of hydrate particle migration and deposition in special pipelines is established based on the computational fluid dynamics (CFD)-discrete element solver (DEM)-application programming interface (API) method, which can be used to carry out real-time visualization calculations of hydrate particles. Simultaneously, this paper reveals the mechanism of hydrate particle migration and deposition at the diameter reduction and direction variation, which provides a new idea for the design of the pipe. Furthermore, for the pipe with diameter reduction + direction variation, the entire process of deposition blockage is simulated, and dangerous locations of pipe clogging are identified. The simulation results found that there is a maximum hydrate deposition particle diameter (MHDPD) for hydrate deposition in the pipe. The results of this work may provide valuable references for accurate prediction of particle deposition in deepwater development.

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.

Challenges as catalysts: how Waymo’s Open Dataset Challenges shape AI development

AbstractArtificial intelligence (AI) and machine learning (ML) are becoming increasingly significant areas of research for scholars in science and technology studies (STS) and media studies. In March 2020, Waymo, Google/Alphabet’s autonomous vehicle project, introduced the ‘Open Dataset Virtual Challenge’, an annual competition leveraging their Waymo Open Dataset. This freely accessible dataset comprises annotated autonomous vehicle data from their own Waymo vehicles. Yearly, Waymo has continued to host iterations of this challenge, inviting teams of computer scientists to tackle evolving machine learning and vision problems using Google's data and tools. This article analyses these challenges, situating them within the context of the ‘Grand Challenges’ of artificial intelligence (AI), which aimed to foster accountable and commercially viable advancements in the late 1980s. Through two exploratory workshops, we adopted a ‘technographic’ approach to examine the pivotal role of challenges in the development and political economy of AI. Serving as an organising principle for the AI innovation ecosystem, the challenge connects companies and external collaborators, driving advancements in specific machine vision domains. By exploring six key themes—interface methods, incrementalism, metrics, AI vernacular, applied domains, and competitive advantages—the article illustrates the role of these challenges in shaping AI research and development. By unpacking the dynamic interaction between data, computation, and labour, these challenges serve as catalysts propelling advancements towards self-driving technologies. The study reveals how challenges have historically and presently shaped the evolving landscape of self-driving and AI technologies.