A multilayer composite coating, comprised of alternating Ni + CrAlYSiHfN sub-layers and AlxN sub-layers, was manufactured by magnetron sputtering. The multilayer is composed of β-NiAl, α-Cr and AlN phases after annealing, and their volume fractions are 72 vol.%, 12 vol.% and 16 vol.%, respectively. Due to doping with nitrides, the CTE of multilayer coating is reduced to around 12.7*10−6/K. The lower CTE mismatch allows multilayer coating to exhibit good resistance to oxide scale spalling in thermal cycling at 1100 °C. The θ-to-α Al2O3 transformation in oxide scale caused a small drop of residual stresses on multilayer coating in initial oxidation stage.

Mg is an abundant and attractive metal, and it can be used for many energy-efficient and environmental-friendly applications in transportation, communication, hydrogen storage and biodegradable products sectors. Considerable efforts have been made in the past 20 years for wider applications of magnesium alloys, and a remarkable progress is achieved on the design and development of cost-effective alloys with improved properties. A significant achievement is also made for the fundamental understanding of alloy microstructures, deformation mechanisms, precipitation processes, alloying effects on formability, and processing-microstructure-property relationships.

Evaluation and benchmarking of real-time skin detectors remain challenging because of multiple evaluation attributes that must be considered. Numerous evaluation and benchmarking techniques have been proposed, but they exhibit several limitations. Fixing multiple attributes based on benchmarking approaches by using other attributes limits reliable real-time skin detection. This paper presents comprehensive insights into the evaluation and benchmarking of real-time skin detectors on the basis of two critical directions. Current evaluation criteria highlight conflicting issues and benchmarking techniques to identify weak points, and possible solutions are discussed. The findings are as follows: (1) open issues and challenges to evaluation and benchmarking are emphasized; and (2) decision making using multiple criteria such as reliability, time complexity, and error rate within a dataset is used for evaluating and benchmarking real-time skin detectors to come up with solutions for future directions.