Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) ...or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.
Edge intelligence is a key enabler for IIoT as it offers smart cloud services in close proximity to the production environment with low latency and less cost. The need for ubiquitous communication, ...computing, and caching resources in 5G beyond will lead to a growing demand to integrate heterogeneous resources into the edge network. Furthermore, distributed edge services can make resource transactions vulnerable to malicious nodes. Ensuring secure edge services under complex industrial networks is a big challenge. In this article, we present an edge intelligence and blockchain empowered IIoT framework, which achieves flexible and secure edge service management. Then we propose a cross-domain sharing inspired edge resource scheduling scheme and design a credit-differentiated edge transaction approval mechanism. Numerical results indicate that the proposed schemes bring significant improvement in both edge service cost and service capacities.
Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. ...However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.
The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower ...the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the etiological agent responsible for the global COVID-19 (coronavirus disease 2019) outbreak. The main protease of SARS-CoV-2, M
, is ...a key enzyme that plays a pivotal role in mediating viral replication and transcription. We designed and synthesized two lead compounds (
and
) targeting M
Both exhibited excellent inhibitory activity and potent anti-SARS-CoV-2 infection activity. The x-ray crystal structures of SARS-CoV-2 M
in complex with
or
, both determined at a resolution of 1.5 angstroms, showed that the aldehyde groups of
and
are covalently bound to cysteine 145 of M
Both compounds showed good pharmacokinetic properties in vivo, and
also exhibited low toxicity, which suggests that these compounds are promising drug candidates.
Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of ...digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.
Soft and stretchable electronic devices are important in wearable and implantable applications because of the high skin conformability. Due to the natural biocompatibility and biodegradability, silk ...protein is one of the ideal platforms for wearable electronic devices. However, the realization of skin‐conformable electronic devices based on silk has been limited by the mechanical mismatch with skin, and the difficulty in integrating stretchable electronics. Here, silk protein is used as the substrate for soft and stretchable on‐skin electronics. The original high Young's modulus (5–12 GPa) and low stretchability (<20%) are tuned into 0.1–2 MPa and > 400%, respectively. This plasticization is realized by the addition of CaCl2 and ambient hydration, whose mechanism is further investigated by molecular dynamics simulations. Moreover, highly stretchable (>100%) electrodes are obtained by the thin‐film metallization and the formation of wrinkled structures after ambient hydration. Finally, the plasticized silk electrodes, with the high electrical performance and skin conformability, achieve on‐skin electrophysiological recording comparable to that by commercial gel electrodes. The proposed skin‐conformable electronics based on biomaterials will pave the way for the harmonized integration of electronics into human.
Silk protein is plasticized and demonstrated as a soft and stretchable substrate for skin‐conformable stretchable electronics. The plasticization is enabled by the addition of CaCl2 and ambient hydration, and the mechanism is investigated using molecular dynamics simulation. Moreover, highly stretchable (>100%) silk electrodes are fabricated for on‐skin electrophysiological measurements. This progress will enable more biomaterial‐based wearable electronics.
The surface plasmon resonance (SPR) phenomenon is of wide interest due to its sensitivity to changes in surface refractive index for the label-free, highly sensitive and rapid detection of ...biomarkers. This paper reviews research progress on SPR biosensors modified with different substrate structures and surface materials, surface plasmon resonance imaging (SPRI), and SPR-enhanced electrochemiluminescent (ECL) biosensors for applications in biosensing in the last five years. This paper focuses on the research on the application of the SPR phenomenon in the field of bio-detection, reviews the sensing characteristics of SPR biosensors with substrate structures of prisms, gratings, and optical fibers, and summarizes and analyzes the sensitivity and interference resistance of SPR sensors with surface modification of different materials (high-refractive index dielectric films, metallic micro- and nanostructures, and surface antifouling materials). Considering that imaging is an important tool for biomedical detection, this paper reviews the research progress on SPRI technology in the field of biomedical detection. In addition, this paper also reviews the research progress on SPR-enhanced ECL biosensors in the field of biosensing. Finally, this paper provides an outlook on the development trends of biosensing technology in terms of portable high-precision SPR sensors, reduction of self-loss of thin film materials, optimization of image processing techniques and simplification of electrode modification for ECL sensors.
The surface plasmon resonance (SPR) phenomenon is of wide interest due to its sensitivity to changes in surface refractive index for the label-free, highly sensitive and rapid detection of biomarkers.
Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging big data and Artificial Intelligence (AI) technologies show ...significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.
Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. ...However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high‐quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn.
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