It is extremely challenging to directly observe how the relative position of the nanosheet changes the charge transport in the channel. Previous work on graphene stacking strain sensors relies on nanosheet slip to detect small mechanical signals. However, the direct experimental verification evidence is still inadequate. In this work, the sliding conductive transmission of graphene nanosheets slip is directly measured through an improved in situ transmission electron microscopy observation technique. By accurately manipulating the atomic scale of graphene nanosheets to achieve nanoscale sliding, the resistance change between nanosheets in the in situ observation system can be directly measured. Besides, a mechanical sensor based on graphene layer structure is designed, which demonstrates a high gauge factor (4303 at maximum strain of 93.3%), negligible hysteresis (5–10%), and excellent stability over 3000 stretch–release cycles. Besides, the precise control of characteristics offers a practical approach of retaining high stability from device to device. This strain sensor is applied in various cases, especially the health monitor of the human cervical vertebra.

Traditional general architecture chips have shown excessive power consumption and insufficient functional redundancy in some customized applications. Flexible electronics also call for customized chips in smart wearable devices. Common chips in intelligent systems process digital signals, and continuous operation of the system clock brings great power consumption. Herein, an intelligent gesture recognition system is developed by combining the flexible 2D materials and an analog computing chip. The pressure‐sensitive sponge with graphene fillings is proposed as a piezoresistive sensor. A nine‐sensor array is used to detect the pressure field distribution caused by hand movement. To get rid of the power consumption caused by the system clock, a novel analog‐computing customized chip which adopts a near‐sensor processing architecture is proposed. It implements the binary neural network algorithm with an analog circuit and completes the recognition of the transmission signal at the hardware level. The chip possesses a low power consumption which is less than 1.8 mW. Moreover, a glove assembled by highly pressure‐sensitive material recognizes mute gestures including Arabic numerals 0–9, with a recognition rate as high as 98.5%. Herein, the prospects for the application of customized smart chips in the field of smart wearable electronics are illuminated.

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object‐oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) have been used for meta‐data management on the Web. With the rapid development of Web‐based applications, the growth of RDF(S) data is increasing dramatically. In the context of open Web, uncertainty naturally arises in RDF and RDFS (RDF(S) in short). To manage large‐scale fuzzy RDF(S) data efficiently and effectively, we propose, in this paper, a fuzzy RDF(S) storage schema with fuzzy HBase databases (FHDBs). On the basis, we investigate how to query in FHDBs. We propose a set of query algorithms. We implement a prototype system to demonstrate the feasibility of our approach.

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