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Emulsion-templated polymerizations are an attractive route to prepare porous materials that possess broad tunability by controlling the features of the emulsion template. Emulsion templated polymer materials possessing shape memory behavior have also been reported, usually using (meth)acrylate monomers. However, achieving shape memory properties in emulsion templated materials with polymers that do not possess accessible thermal transitions, including polydimethylsiloxane (PDMS), remains challenging. Here, porous PDMS materials have been prepared with stearic acid within the continuous phase of the emulsion template. The inclusion of stearic acid imparts the material with a transition temperature of ∼70 °C, and the porous materials in this work obtained fixity >90% and recovery >95% over multiple shape memory cycles. These results demonstrate how low glass-transition temperature emulsion-templated polymer materials can easily be given shape memory properties. This work should be a starting point for studies of elastomeric emulsion-templated polymer materials in applications, including in soft robotics. 

In an indoor space, determining a person's mobility patterns has research significance and applicability in real-world scenarios. When mobility patterns are determined, layout optimization can be implemented in indoor spaces to improve efficiency. This research aimed to determine a person's path using Received Signal Strength Indicator (RSSI) data collected from Bluetooth-enabled mobile devices. Mobile app-based mobility detection using Bluetooth RSSI has the advantage of low cost and easy implementation. The research methodology involves developing a Bluetooth RSSI mobility application system to determine the path of a moving mobile device using a vectorized algorithm. The paper presents challenges in creating such a software system, its architecture, the data collection and analysis process, and the results of mobility detection. This research shows that Bluetooth-enabled mobile devices and Bluetooth RSSI data can be used to determine the path in an indoor space with workable accuracy. 

We consider the problem of model selection using the Minimum Description Length (MDL) criterion for distributions with parameters on the hypersphere. Model selection algorithms aim to find a compromise between goodness of fit and model complexity. Variables often considered for complexity penalties involve number of parameters, sample size and shape of the parameter space, with the penalty term often referred to as stochastic complexity. Current model selection criteria either ignore the shape of the parameter space or incorrectly penalize the complexity of the model, largely because typical Laplace approximation techniques yield inaccurate results for curved spaces. We demonstrate how the use of a constrained Laplace approximation on the hypersphere yields a novel complexity measure that more accurately reflects the geometry of these spherical parameters spaces. We refer to this modified model selection criterion as spherical MDL. As proof of concept, spherical MDL is used for bin selection in histogram density estimation, performing favorably against other model selection criteria.