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The application of the Internet of Things in agricultural development usually occurs via a monitoring network that consists of a large number of sensor nodes, thus gradually transforming agriculture from a human-oriented and single-machine-centric production model to an information-and software-centric production model. Due to the large area coverage of agriculture and the variety of production objects, if all farmland perception information is gathered into the cloud server, the server will exert greater pressure on the network, which reduces the speed of response to event processing. This problem may be perfectly solved by the recent emergence of Edge computing, which can share the load of the cloud server and reduce the delay. Edge computing has prospects in agricultural applications, such as pest identification, safety traceability of agricultural products, unmanned agricultural machinery, agricultural technology promotion, and intelligent management. The application of the Agricultural Internet of Things integrates artificial intelligence, the Internet of Things, and blockchain and Virtual/Augmented Reality technologies. This paper primarily reviews the application of Edge computing in the Agricultural Internet of Things and investigates the combination of Edge computing and Artificial Intelligence, blockchain and Virtual/Augmented reality technology. The challenges of Edge computing task allocation, data processing, privacy protection and security, and service stability in agriculture are reviewed. The future development direction of Edge computing in the Agricultural Internet of Things is predicted.

IL-6 produced by human fibroblast-like synoviocytes (HFLS) promotes rheumatoid arthritis (RA), while lncRNA DILC regulates liver cancer stem cells by inhibiting IL-6. Therefore, lncRNA DILC may participate in RA. In the present study, we found that plasma lncRNA DILC was down-regulated, while IL-6 was up-regulated in RA patients than in healthy controls. Plasma levels of lncRNA DILC and IL-6 were significantly and inversely correlated only in RA patients. Overexpression of lncRNA DILC resulted in promoted apoptosis of HFLS isolated from RA patients, while lncRNA DILC siRNA silencing played an opposite role. In addition, overexpression of lncRNA DILC also resulted in inhibited IL-6 expression in HFLS isolated from RA patients. Therefore, lncRNA DILC may participate in RA by inducing apoptosis of HFLS and down-regulating IL-6.

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Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

Wireless sensor networks (WSNs) have been expected to improve the capability of capturing mechanical vibration dynamic behaviors and evaluating the current health status of equipment. While the expectation for mechanical vibration monitoring using WSNs has been high, one of the key limitations is the limited lifetime of batteries for sensor node. The energy harvesting technologies have been recently proposed. One of them shares the same main idea, that is, energy harvesting from ambient vibration can be converted into electric power. Employing the vibration energy harvesting, a novel self-powered wireless sensor node has been developed to measure mechanical vibration in this paper. The overall architecture of node is proposed. The wireless sensor node is described into four main components: the energy harvesting unit, the microprocessor unit, the radio transceiver unit, and accelerometer. Moreover, the software used to control the operation of wireless node is also suggested. At last, in order to achieve continuous self-powered for nodes, two operation modes including the charging mode and discharging mode are proposed. This design can effectively solve the problem of continuous supply power of sensor node for mechanical vibration monitoring.