Mixture models are frequently used for modeling complex data. An extension of the EM algorithm, here called ECME, is proposed to compute the maximum likelihood estimate of parameters of symmetric -stable mixture model (S SMM). Comprehensive simulation studies are performed to show the performance of the proposed ECME algorithm. The robustness of the S SMM is investigated by simulations when it is used to model data generated from mixture of exponential power and t distributions. Both proposed ECME and Bayesian approaches are applied to three sets of real data, which shows that the proposed ECME algorithm outperforms the Bayesian paradigm for all three sets. Also, the S SMM is compared with the mixture of normal, skew normal, t, and skew t distributions for modeling four sets of real data. It turns out that the S SMM works as well as or better than above models. This can be considered as S SMM capability in robust mixture modeling.

Healthcare facility retrofit projects are among the most challenging types of construction. Such projects are often disruptive to patients and can cause significant patient safety issues. The aim of this study was to investigate current practices of retrofitting healthcare facilities, with a particular focus on patient safety and energy efficiency. To achieve this, the study followed a systematic approach to investigating the issues involved with retrofitting healthcare facilities. Three case studies were conducted, and data were collected through interviews, shadowing, site visits, and cognitive walkthroughs. Direct observation of retrofit projects was also undertaken to acquire firsthand knowledge of the retrofit process and to explore possible solutions associated with current practices. As more facilities consider the decision to retrofit, it is essential to provide a practical rationale for pursuing retrofit projects that are energy efficient and safe for patients.

A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm. The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages. In the first stage, the K-Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy C-Means algorithm is applied on the centers obtained in the first stage. Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy. In addition, the mentioned clustering algorithms are applied to a few benchmark datasets in order to verify their performances. Finally, a class of Minkowski distances is used to determine the influence of distance on the clustering performance.