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The SiCf/SiC composites/Ni-based superalloys dissimilar joining encountered the problem of huge thermal mismatch and uncontrollable interfacial reactions. Two novel composite fillers were designed for the dissimilar joining. An AuCuTi/Mo/AuCuTi filler enabled a complete hindrance on the atomic exchange by a 50 μm Mo interlayer and an effective stress relief by multiple layers of the ductile (Au, Cu) solid solution and rigid Mo insert. Furthermore, the addition of B introduced an extra dimensionality to control the diffusion of interstitial atoms (Ni, Cr, and Si) and some TiB whispers to add extra nucleation sites for the Ni-compounds, by which way an easier and more accurate method to control the distribution of metallic atoms were obtained. The load-carrying of joints increased by 17.7% when using an AuCuTiB/Mo/AuCuTiB filler, because of the distribution of intermetallic compounds was homogenized and fined, and the interfacial region changed from Ti3SiC2 to TiC + Ni-compounds.

A new Au-Cu-Ti filler featuring superior mechanical properties was developed to enable the brazing of a SiCf/SiC composite (CMCs) to itself and a Ni-based superalloy (GH536). The progression of the interfacial reactions was studied using a combination of thermodynamic calculations and experimental observations. It was found that the interfacial reaction was Ti-dominant at the early brazing stage and then gradually transformed to Ni-dominant with the continuous dissolution of the GH536 substrate. Thus, the typical microstructure of GH536/Ti-Ni-Cr-Fe+(Au, Cu)ss + MoNiSi/Ni-Cr-Fe-Si-C (Ni2Si + Fe2Si + Cr3C2)+(Au, Cu)ss/Ni2Si + TiC+(Au, Cu)ss/ Cr3C2+Ni2Si + TiC + Fe2Si/CMCs could be described by the following three stages: a Ti-dominated stage, full interdiffusion stage, and Ni-dominated stage. A maximum shear strength of 36 MPa was obtained for joints brazed at 1050℃ for 10 min, at which a failure occurred at the CMCs/brazing seam interface. The control of the interfacial reactions and the stress relaxation of (Au, Cu)ss contributed to the superior mechanical performance of the composite.

Clustering analysis, as one of the important methods in data mining technology, merely provides a method for the research and analysis of large amounts of data. Starting with the most important nodes, this paper divides clustering based on data field characteristics. The sensitivity pruning algorithm is then used to further adjust and optimize the structure of the fuzzy neural network, allowing the network to automatically learn the structure and parameters of the system in different environments and obtain the optimal control rules. Finally, the clustering function brings the algorithm’s output result to a close. The experimental results show that the adaptive clustering algorithm of complex networks presented in this paper can effectively improve network cluster division, reduce algorithm time complexity, and avoid the problem of providing the number of cluster structures in advance. This method’s cluster structure efficiency can reach 97.6 percent, and its highest clustering accuracy can reach 96.8 percent. The adaptive clustering algorithm proposed in this paper not only overcomes the traditional algorithm’s flaws, such as the need to predetermine the number of clusters and the clustering result being dependent on the initial clustering center selection, but also has ideal clustering accuracy. This study introduces a novel and more effective method for addressing the difficult problems of practical complex control systems.

Ranking the spreading influence of nodes is crucial for developing strategies to control the spreading process on complex networks. In this letter, we define, for the first time, a remaining minimum degree (RMD) decomposition by removing the node(s) with the minimum degree iteratively. Based on the RMD decomposition, a weighted degree (WD) is presented by utilizing the RMD indices of the nearest neighbors of a node. WD assigns a weight to each degree of this node, which can distinguish the contribution of each degree to the spreading influence. Further, an extended weighted degree (EWD) centrality is proposed by extending the WD of the nearest neighbors of a node. Assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive experiments on a series of synthetic and real networks to comprehensively evaluate the performance of EWD and other eleven representative measures. The experimental results show that EWD is a relatively efficient measure in running efficiency, it exposes an advantage in accuracy in the networks with a relatively small degree heterogeneity, as well as exposes a competitive performance in resolution.

With the wide application of Automated Guided Vehicle (Hereinafter referred to as AGV) in modern manufacturing, logistics and transportation industries, improving the work efficiency of AGV has become a major focus of the industry research. In order to reduce the limitation of two factors—AGV optimal running path search and path search speed on the efficiency of AGV, this paper based on the A-star algorithm uses the characteristics of jump point search to improve the node search mode and the search speed, and adds the angle evaluation cost function to the cost function of A-star algorithm to find the path with the least inflection point, so as to quickly find the optimal path. And the simulation experiments in this paper show that the optimized A-star algorithm can search for the optimal path, of which path search speed is faster than that of the A-star algorithm.

With the rapid development of Internet technology, the online learning environment is centered on learners and no longer limited by time and space, making the demand for online learning increasingly. However, in the face of the Internet’s learning resources, learners often have no choice, and problems in the online learning process can not be solved in time, so it is imperative to construct a personalized learning environment that meets the characteristics of learners. Personalized learning is based on educational big data. Big data provides technical support for personalized learning, and provides a new way for traditional personalized learning. Personalized learning in the context of big data is based on learners. Taking into account the learning characteristics of learners, it is a breakthrough in traditional learning methods. With the advent of the era of big data and the era of artificial intelligence, the learning methods, teaching methods and cognitive styles of distance education have undergone major changes. The characteristics of the times require a new connotation for learning support services, which is traditional, unified and fixed. The learning support service has turned to the development of personalized instructional design, course management and learning evaluation services. A personalized distance education learning support service system was constructed. This paper mainly studies the online personalized learning platform system based on educational data mining. The system consists of four modules: management services, information and consulting services, resource services and learning process services. In view of the difficulties of distance learning learners, lack of learning time, self-learning ability and weak self-management ability, data mining technology can be used to analyze and mine learning process data and historical data accumulated in the process of distance education. The learner’s learning state and personality characteristics provide the main tailored learning content and learning path to guide learners to learn more effectively in the online learning environment, overcome learning obstacles and achieve learning goals.

Changes in the sediment availability in the Gobi Desert are not well estimated under current climate and environmental conditions. Additionally, the contributions of dustfall and dust-sized fractions produced by physical weathering in the Gobi Desert and their significance on gobi surface equilibrium are still poorly understood. The objective of this study is to quantify the amounts of dust-sized fractions deposited via dustfall and produced by physical weathering in the Gobi Desert and to evaluate the roles of these two processes in maintaining the gobi surface equilibrium. Physical weathering experiments and dustfall collections are conducted from 20th October 2017 to 20th October 2018 in the Ala Shan Gobi, a representative area of the Gobi Desert in Central Asia. The experimental results showed that dustfall on Gobi Desert is 1093 g m−2, simultaneously 944 g m−2 of the dust-sized fractions (

Digital coding-mask technique based on digital micro-mirror devices (DMD) is proposed in this paper. The fundamental rule of digital coding-mask technique is to modulate the incident light intensity by adjusting the transmittance of the units on the coding-mask. The transmittance is controlled by the apertures on the units of the coding-mask. Lohmann’s III coding method and error diffusion coding method are employed to coding mask, and wavelet transformation is used to suppress the background noise of the mask image. Real-time control on the image of the digital coding mask can be realized by loading the coded mask image to DMD, which is driven by a computer. Digital coding-mask technique gives full play of the advantages of DMD, such as real time and flexibility. In addition, the digital coding-mask technique is helpful to deal with the problem of mask aberration, which is caused by the nonlinear effect in the process of projection and exposure. This technique can also make use of optimization algorithm to suppress the background noise of the digital coding-mask images so that the quality of the relief structure of photoresist is improved.