The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customi
zed production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.
We have witnessed an unprecedented public health crisis caused by the new coronavirus disease (COVID-19), which has severely affected medical institutions, our common lives, and social-economic activities. This crisis also reveals the brittleness of
existing medical services, such as over-centralization of medical resources, the hysteresis of medical services digitalization, and weak security and privacy protection of medical data. The integration of the Internet of Medical Things (IoMT) and blockchain is expected to be a panacea to COVID-19 attributed to the ubiquitous presence and the perception of IoMT as well as the enhanced security and immutability of the blockchain. However, the synergy of IoMT and blockchain is also faced with challenges in privacy, latency, and context-absence. The emerging edge intelligence technologies bring opportunities to tackle these issues. In this article, we present a blockchain-empowered edge intelligence for IoMT in addressing the COVID-19 crisis. We first review IoMT, edge intelligence, and blockchain in addressing the COVID-19 pandemic. We then present an architecture of blockchain-empowered edge intelligence for IoMT after discussing the opportunities of integrating blockchain and edge intelligence. We next offer solutions to COVID-19 brought by blockchain-empowered edge intelligence from 1) monitoring and tracing COVID-19 pandemic origin, 2) traceable supply chain of injectable medicines and COVID-19 vaccines, and 3) telemedicine and remote healthcare services. Moreover, we also discuss the challenges and open issues in blockchain-empowered edge intelligence.
Intermediate/Extreme mass ratio inspiral (IMRI/EMRI) system provides a good tool to test the nature of gravity in strong field. We construct the self-force and use the self-force method to generate accurate waveform templates for IMRIS/EMRIs on quasi
-elliptical orbits in Brans-Dicke theory. The extra monopole and dipole emissions in Brans-Dicke theory accelerate the orbital decay, so the observations of gravitational waves may place stronger constraint on Brans-Dicke theory. With a two-year observations of gravitational waves emitted from IMRIs/EMRIs with LISA, we can get the most stringent constraint on the Brans-Dicke coupling parameter $omega_0>10^5$.
Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main chall
enges: privacy and security risks, information silo, and pricing mechanisms and incentives. The recent advances of blockchain bring opportunities to address the challenges of services computing due to its build-in encryption as well as digital signature schemes, decentralization feature, and intrinsic incentive mechanisms. In this paper, we present a survey to investigate the integration of blockchain with services computing. The integration of blockchain with services computing mainly exhibits merits in two aspects: i) blockchain can potentially address key challenges of services computing and ii) services computing can also promote blockchain development. In particular, we categorize the current literature of services computing based on blockchain into five types: services creation, services discovery, services recommendation, services composition, and services arbitration. Moreover, we generalize Blockchain as a Service (BaaS) architecture and summarize the representative BaaS platforms. In addition, we also outline open issues of blockchain-based services computing and BaaS.
Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it
enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.
One of the typical features of Majorana zero mode (MZM) at the end of topological superconductor is a zero-bias peak in the tunneling spectroscopy of the normal lead-superconductor junction. In this paper we study on a model with one phonon mode coup
ling to the superconductor lead of the normal lead-superconductor junction, which can be viewed as an electron-lead/phonon-coupled-MZM/hole-lead structure. The phonon-coupled MZM acts as a series of channels in which electron can turn into hole by absorbing and emitting phonons. These channels present in the local density of states (LDOS) as a series of stripes, generating the corresponding peaks in the tunneling spectroscopy. In LDOS, the electron-phonon interaction narrows and redistributes the weight among stripes. In the tunneling spectroscopy, the heights of peaks present a feature of the multi-phonon process. With these investigations, our work illuminates the mechanism of phonon-assisted Andreev reflection at a Majorana zero mode.
The 3D topological insulator (TI) PN junction under magnetic fields presents a novel transport property which is investigated both theoretically and numerically in this paper. Transport in this device can be tuned by the axial magnetic field. Specifi
cally, the scattering coefficients between incoming and outgoing modes oscillate with axial magnetic flux at the harmonic form. In the condition of horizontal mirror symmetry, the initial phase of the harmonic oscillation is dependent on the parities of incoming and outgoing modes. This symmetry is broken when a vertical bias is applied which leads to a kinetic phase shift added to the initial phase. On the other hand, the amplitude of oscillation is suppressed by the surface disorder while it has no influence on the phase of oscillation. Furthermore, with the help of the vertical bias, a special (1,-2) 3D TI PN junction can be achieved, leading to a novel spin precession phenomenon.
In analogy with light refraction at optical boundary, ballistic electrons also undergo refraction when propagate across a semiconductor junction. Establishing a negative refractive index in conventional optical materials is difficult, but the realiza
tion of negative refraction in electronic system is conceptually straightforward, which has been verified in graphene p-n junctions in recent experiments. Here, we propose a model to realize double refraction and double focusing of electric current by a normal-hexagonal semiconductor junction. The double refraction can be either positive or negative, depending on the junction being n-n type or p-n type. Based on the valley-dependent negative refraction, a spin splitter (valley splitter) is designed at the p-n junction system, where the spin-up and spin-down electrons are focused at different regions. These findings may be useful for the engineering of double lenses in electronic system and have underlying application of spin splitter in spintronics.
We study the electron transport through the graphene PNP junction under a magnetic field and show that modes mixing plays an essential role. By using the non-equilibrium Greens function method, the space distribution of the scattering state for a spe
cific incident modes as well the elements of the transmission and reflection coefficient matrixes are investigated. All elements of the transmission (reflection) coefficient matrixes are very different for a perfect PNP junction, but they are same at a disordered junction due to the mode mixing. The space distribution of the scattering state for the different incident modes also exhibit the similar behaviors, that they distinctly differ from each other in the perfect junction but are almost same in the disordered junction. For a unipolar junction, when the mode number in the center region is less than that in the left and right regions, the fluctuations of the total transmission and reflection coefficients are zero, although each element has a large fluctuation. These results clearly indicate the occurrence of perfect mode mixing and it plays an essential role in a graphene PNP junction transport.
We propose and implement a lattice scheme for coherently manipulating atomic spins. Using the vector light shift and a superlattice structure, we demonstrate experimentally the capability on parallel spin addressing in double-wells and square plaquet
tes with subwavelength resolution. Quantum coherence of spin manipulations is verified through measuring atom tunneling and spin exchange dynamics. Our experiment presents a building block for engineering many-body quantum states in optical lattices for realizing quantum simulation and computation tasks.
