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A thermodynamic model Mgx(Xs, Mg)6(Xl, Mg)8 (Xs and Xl are elements smaller and larger than Mg) for long-period stacking ordered phases (LPSOs) was proposed based on two key factors: the Xs6Xl8-type L12 clusters and the variation of chemical compositions. In general, all available LPSOs can be described with this model. As a representative system, Mg-Y-Zn with three LPSOs was investigated using the CALPHAD (calculation of phase diagram) approach aided with first-principles calculations. Two new three-phase equilibria were predicted and were validated by key experiments. The model-based descriptions will be the basis for the research and development of magnesium alloys.

Mobile devices, along with wearable sensors, allow patients to access healthcare services from anywhere at any time. The longstanding constraints of computational capability and storage space on mobile devices can be alleviated by outsourcing computation- or data-intensive tasks to remote cloud centers. Thus, mobile cloud computing (MCC) has been recognized as a promising approach to provide pervasive healthcare services to people in their daily life. As the development and adoption of MCC techniques in healthcare, new optimization strategies have been explored and studied to help mobile cloud healthcare services to be deployed in a more effective and efficient manner. In this survey, we demonstrate how MCC techniques have been extensively deployed in various healthcare applications and, specifically, describe the general architecture and design considerations one should take into account while designing an MCC for healthcare scenarios. Given a large number of factors that may affect the performance of the MCC and even result in catastrophic consequences in healthcare, this paper presents the state-of-the-art optimization methods on the MCC for meeting the diverse priorities and achieving the optimal tradeoff among multiple objectives. Finally, the security and privacy issues of the MCC in healthcare are also discussed.

An overview about one thermodynamic database of multi-component Mg alloys is given in this work. This thermodynamic database includes thermodynamic descriptions for 145 binary systems and 48 ternary systems in 23-component (Mg–Ag–Al–Ca–Ce–Cu–Fe–Gd–K–La–Li–Mn–Na–Nd–Ni–Pr–Si–Sn–Sr–Th–Y–Zn–Zr) system. First, the major computational and experimental tools to establish the thermodynamic database of Mg alloys are briefly described. Subsequently, among the investigated binary and ternary systems, representative binary and ternary systems are shown to demonstrate the major feature of the database. Finally, application of the thermodynamic database to solidification simulation and selection of heat treatment schedule is described.

The thermodynamic descriptions of the perovskite phase SrZrO3 and the TbBr3-KBr molten salt system were carried out using the available experimental information. Special attention was paid to the structural behavior of SrZrO3 and the decomposition of K3TbBr6 at low temperature, respectively, to illustrate how to select an appropriate thermodynamic model based on crystal structure and chemistry information, how to identify and resolve the inconsistency between various kinds of experimental data, and how to use thermodynamic modeling as a basic tool in the development and optimization of materials and process. In the present work, different structures of SrZrO3 were explained by thermodynamic calculation and confirmed with experimental technologies. The decomposition of the compound K3TbBr6at about 593 K was detected by the present thermodynamic calculation and the new complementary experimental measurements. Comparison between the calculated and measured phase diagrams as well as thermodynamic quantities provided the final test of the overall consistency between the reliable experimental information and the present modeling and thermodynamic computation.