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/0000-0002-9922-7912, Mahmood, I and Dehghani-Sanij, AA (2018) Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors.

The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible-Exposed-Infectious-Recovered (SEIR) models that operate on a national level. Here we introduce the Flu And Coronavirus Simulator (FACS), a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas in a region. Using FACS, we can model Covid-19 spread at the local level, and provide estimates of the spread of infections and hospital arrivals for different scenarios. We validate the simulation results with the ICU admissions obtained from the local hospitals in the UK. Such validated models can be used to support local decision-making for an effective health care capability response to the epidemic.

Many different types of ranking methods based on the score and accuracy functions of intuitionistic fuzzy values (IFVs) exist in the literature. The notion of knowledge bases, as in the case of rough set theory, is very handy to show that every ranking technique produces a unique classification of IFVs with a unique order among the classes. This means these rankings give rise to unique knowledge bases. Therefore, ranking of IFVs by two or more distinct techniques may produce different results. In this study, a graphical ranking method based on the uncertainty index and entropy is proposed. This approach is tested on several numerical examples existing in the literature and shown to be intuitive and convenient for applications in real‐life scenarios.

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