The entropic alloys can be categorized into four types of alloys, e.g., high-entropy alloys, medium-entropy alloys, low-entropy alloys, and pure metals. The high-entropy alloys are a new kind of materials where the mixing entropy plays an important role in the phase formation. Because of the unique structures, the entropic alloys exhibit many outstanding properties, which even break the performance limits of traditional materials, including the excellent low-temperature properties. The mechanical properties of the entropic alloys serving at low temperature are mainly introduced in this chapter, including strength, plasticity, fracture behaviors, and impact resistance, and the reasons for these behaviors reported in recent years are also summed up.

With the large amount of complex network data becoming available in the web, link prediction has become a popular research field of data mining. We focus on the link prediction task which can be formulated as a binary classification problem in social network. To treat this problem, a sparse semi-supervised classification algorithm called Self Training Semi-supervised Truncated Kernel Projection Machine (STKPM), based on empirical feature selection, is proposed for link prediction. Experimental results show that the proposed algorithm outperformed several outstanding learning algorithms with smaller test errors and more stability.

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The magnetic sensors based on soft magnetic efects of amorphous ibers are one of the highlights in scientiic research in recent years. The amorphous ibers not only have excellent mechanical properties but also have unique magnetic properties, such as high permeability. As a result, sensors made of this kind of material can show the characteristics of sensitivity and durability. The processing and their advantages and disadvantages are mainly introduced in the chapter, and the properties reported in recent years are also summed up, including mechanical behavior, magnetic properties and shape-memory efects.