Hybrid nodal surface and nodal line phonons in solids

Phonons have provided an ideal platform for a variety of intriguing physical states, such as non-abelian braiding and Haldane model. It is promising that phonons will realize the complicated nodal states accompanying with unusual quantum phenomena. Here, we propose the hybrid nodal surface and nodal line (NS+NL) phonons beyond the single genre nodal phonons. We categorize the NS+NL phonons into two-band and four-band situations based on symmetry analysis and compatibility relationships. Combing database screening with first-principles calculations, we identify the ideal candidate materials for realizing all categorized NS+NL phonons. Our calculations and tight-binding models further demonstrate that the interplay between NS and NL induces unique phenomena. In space group 113, the quadratic NL acts as a hub of the Berry curvature between two NSs, generating ribbon-like surface states. In space group 128, the NS serve as counterpart of Weyl NL that NS-NL mixed topological surface states are observed. Our findings extend the scope of hybrid nodal states and enrich the phononic states in realistic materials.Comment: 23+35 pages, 5+44 figures, 1+3 table

Efficient Triangular Interpolation Method: Error Analysis and Applications

The interpolation errors of bivariate Lagrange polynomial and triangular interpolations are studied for the plane waves. The maximum and root-mean-square (RMS) errors on the right triangular, equilateral triangular and rectangular (bivariate Lagrange polynomial) interpolations are analyzed. It is found that the maximum and RMS errors are directly proportional to the (p+1)’th power of kh for both one-dimensional (1D) and two-dimensional (2D, bivariate) interpolations, where k is the wavenumber and h is the mesh size. The interpolation regions for the right triangular, equilateral triangular and rectangular interpolations are selected based on the regions with smallest errors. The triangular and rectangular interpolations are applied to evaluate the 2D singly periodic Green’s function (PGF). The numerical results show that the equilateral triangular interpolation is the most accurate interpolation method, while the right triangular interpolation is the most efficient interpolation method

From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Finally, we suggest several open directions worth investigating for the future popularity of GIoT

Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach

As the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real-time rendering technologies, to offer immersive metaverse services for Vehicular Metaverse Users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large-scale VT migration. To this end, we propose a blockchain-assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large-scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.Comment: Transactions on Emerging Telecommunications Technologies (ISSN: 2161-3915

Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses

The gradual fusion of intelligent transportation systems with metaverse technologies is giving rise to vehicular metaverses, which blend virtual spaces with physical space. As indispensable components for vehicular metaverses, Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs) and facilitate customized metaverse services to VMUs. VTs are established and maintained in RoadSide Units (RSUs) with sufficient computing and storage resources. Due to the limited communication coverage of RSUs and the high mobility of VMUs, VTs need to be migrated among RSUs to ensure real-time and seamless services for VMUs. However, during VT migrations, physical-virtual synchronization and massive communications among VTs may cause identity and location privacy disclosures of VMUs and VTs. In this article, we study privacy issues and the corresponding defenses for VT migrations in vehicular metaverses. We first present four kinds of specific privacy attacks during VT migrations. Then, we propose a VMU-VT dual pseudonym scheme and a synchronous pseudonym change framework to defend against these attacks. Additionally, we evaluate average privacy entropy for pseudonym changes and optimize the number of pseudonym distribution based on inventory theory. Numerical results show that the average utility of VMUs under our proposed schemes is 33.8% higher than that under the equal distribution scheme, demonstrating the superiority of our schemes.Comment: 8 pages, 6 figure

Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study

As the next-generation wireless communication system, Sixth-Generation (6G) technologies are emerging, enabling various mobile edge networks that can revolutionize wireless communication and connectivity. By integrating Generative Artificial Intelligence (GAI) with mobile edge networks, generative mobile edge networks possess immense potential to enhance the intelligence and efficiency of wireless communication networks. In this article, we propose the concept of generative mobile edge networks and overview widely adopted GAI technologies and their applications in mobile edge networks. We then discuss the potential challenges faced by generative mobile edge networks in resource-constrained scenarios. To address these challenges, we develop a universal resource-efficient generative incentive mechanism framework, in which we design resource-efficient methods for network overhead reduction, formulate appropriate incentive mechanisms for the resource allocation problem, and utilize Generative Diffusion Models (GDMs) to find the optimal incentive mechanism solutions. Furthermore, we conduct a case study on resource-constrained mobile edge networks, employing model partition for efficient AI task offloading and proposing a GDM-based Stackelberg model to motivate edge devices to contribute computing resources for mobile edge intelligence. Finally, we propose several open directions that could contribute to the future popularity of generative mobile edge networks

Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes

Postbiotics Derived from L. paracasei ET-22 Inhibit the Formation of S. mutans Biofilms and Bioactive Substances: An Analysis

Globally, dental caries is one of the most common non-communicable diseases for patients of all ages; Streptococcus mutans (S. mutans) is its principal pathogen. Lactobacillus paracasei (L. paracasei) shows excellent anti-pathogens and immune-regulation functions in the host. The aim of this study is to evaluate the effects of L. paracasei ET-22 on the formation of S. mutans biofilms. The living bacteria, heat-killed bacteria, and secretions of L. paracasei ET-22 were prepared using the same number of bacteria. In vitro, they were added into artificial-saliva medium, and used to coculture with the S. mutans. Results showed that the living bacteria and secretions of L. paracasei ET-22 inhibited biofilm-growth, the synthesis of water-soluble polysaccharide and water-insoluble polysaccharide, and virulence-gene-expression levels related to the formation of S. mutans biofilms. Surprisingly, the heat-killed L. paracasei ET-22, which is a postbiotic, also showed a similar regulation function. Non-targeted metabonomics technology was used to identify multiple potential active-substances in the postbiotics of L. paracasei ET-22 that inhibit the formation of S. mutans biofilms, including phenyllactic acid, zidovudine monophosphate, and citrulline. In conclusion, live bacteria and its postbiotics of L. paracasei ET-22 all have inhibitory effects on the formation of S. mutans biofilm. The postbiotics of L. paracasei ET-22 may be a promising biological anticariogenic-agent

On the Use of Hybrid CFIE-EFIE for Objects Containing Closed-Open Surface Junctions

To effectively solve the electromagnetic scattering or radiation properties from the perfect electric conductor (PEC) objects containing closed-open surface junctions, how to establish the hybrid combined field integral equation-electric field integral equation (CFIE-EFIE) is studied, which is different with the existing scheme for the objects where the closed and open parts are separate. Further, it is found that when the integral equation is solved using the method of moments (MoM), if the widely used RWG basis functions are employed to expand the induced surface current, the CFIE-EFIE may give inaccurate numerical results for the objects containing fine structures. The numerical accuracy can be improved by introducing the linear-linear (LL) basis functions. Moreover, to pursue a high computational efficiency, the LL and RWG basis functions are simultaneously used to expand the current on the fine structures and other relatively smooth surfaces respectively, whose validity is verified by numerical results