In recent years, blockchains have obtained so much attention from researchers, engineers, and institutions; and the implementation of blockchains has started to revive a large number of applications ranging from e-finance, e-healthcare, smart home, Internet of Things, social security, logistics and so forth. In the literature on blockchains, it is found that most articles focused on their engineering implementation, while little attention has been devoted to the exploration of theoretical aspects of the system; however, the existing work is limited to model the mining process only. In this paper, a queuing theory-based model is proposed for understanding the working and theoretical aspects of the blockchain. We validate our proposed model using the actual statistics of two popular cryptocurrencies, Bitcoin and Ethereum, by running simulations for two months of transactions. The obtained performance measures parameters such as the Number of Transactions per block, Mining Time of Each Block, System Throughput, Memorypool count, Waiting Time in Memorypool, Number of Unconfirmed Transactions in the Whole System, Total Number of Transactions, and Number of Generated Blocks; these values are compared with actual statistics. It was found that the results gained from our proposed model are in good agreement with actual statistics. Although the simulation in this paper presents the modeling of blockchain-based cryptocurrencies only, the proposed model can be used to represent a wide range of blockchain-based systems.

Gas sensor, as one of the most important devices to detect noxious gases, provides a vital way to monitor the concentration and environmental information of gas in order to guarantee the safety of production. Therefore, researches on high sensitivity, high selectivity, and high stability have become hot issues. Since the discovery of the nanomaterial, it has been increasingly applied to the gas sensor for its distinguishing surface performances. However, 0-D and 1-D nanomaterials, with limited electronic confinement effect and surface effect, cannot reach the requirement for the production of gas sensors. This paper gives an introduction about the current researching progress and development trend of 2-D nanomaterials, analyzes the common forms of 2-D nanoscale structure, and summarizes the mechanism of gas sensing. Then, widely concerned factors including morphological properties and crystalline structure of 2-D nanomaterial, impact of doped metal on the sensibility of gas sensors, impact of symmetry, and working temperature on the selectivity of gas sensors have been demonstrated in detail. In all, the detailed analysis above has pointed out a way for the development of new 2-D nanomaterial and enhancing the sensibility of gas sensors.

With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP during the manufacturing process as well as the in-service procedure. Most common types of defects in the CFRP are debonds and delaminations. It is difficult to detect the inner defects on a complex shaped specimen by using conventional Nondestructive Testing (NDT) methods. In this paper, an unsupervised machine learning method based on wavelet integrated alternating sparse dictionary matrix decomposition (WIASDMD) is proposed to extract the weaker and deeper defects information for CFRP by using the optical pulse thermography (OPT) system. We propose to model the low rank and sparse decomposition jointly in an alternating manner. By incorporating the low rank information into the sparse matrix and vice versa, the weaker defects will be more efficiently extracted from noise and background. In addition, the integrated of wavelet analysis with dictionary factorization enables an efficient time-frequency mining of information and significantly removes the high frequency noise as well as boosts the speed of computations. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped CFRP specimens. A comparative analysis has also been undertaken to study the proposed method against the general OPT nondestructive testing (OPTNDT) methods.