The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.

Brain magnetic resonance (MR) image registration is an important part of brain MR image registration. Practically, there are many images needing to be registered at same time and the requirement for time cost is high. The current image registration methods register images one by one independently, so the methods are called static image registration methods. The methods are time consuming since they need to randomly initialize the relevant parameters before they start to register some image, so they can not meet the requirement of the practical applications for time cost. Through statistical research, we find the differences of the brain MR images are limited due to some reasons such as similar poses, similar brain structures and so on. So, we proposed one dynamic brain MR image registration algorithm combining inheritance idea and PSO. The algorithm can inherit the information of the former image's registration and use it to guide the current image registration to improve the stability of high precision of image registration and reduce time cost for image registration. The experimental results show that this algorithm can obtain higher stability of image registration, higher registration precision and lower time cost.