A detailed survey of the current trends and recent advances in rotary mechanical circulatory support systems is presented in this paper. Rather than clinical reports, the focus is on technological aspects of these rehabilitating devices as a reference for engineers and biomedical researchers. Existing trends in flow regimes, flow control, and bearing mechanisms are summarized. System specifications and applications of the most prominent continuous-flow ventricular assistive devices are provided. Based on the flow regime, pumps are categorized as axial flow, centrifugal flow, and mixed flow. Unique characteristics of each system are unveiled through an examination of the structure, bearing mechanism, impeller design, flow rate, and biocompatibility. A discussion on the current limitations is provided to invite more studies and further improvements.

Shape memory alloys (SMAs) provide compact and effective actuation for a variety of mechanical systems. In this work, the distinguished superelastic behavior of these materials is utilized to develop a passive ankle foot orthosis to address the drop foot disability. Design, modeling, and experimental evaluation of an SMA orthosis employed in an ankle foot orthosis (AFO) are presented in this paper. To evaluate the improvements achieved with this new device, a prototype is fabricated and motion analysis is performed on a drop foot patient. Results are presented to demonstrate the performance of the proposed orthosis.

Over the past decades, numerous techniques have been developed to forecast the temporal evolution of epidemic outbreaks. This paper proposes an approach that combines high resolution agent-based models using realistic social contact networks for simulating epidemic evolution with a particle filter based method for assimilation based forecasting. Agent-based modeling using realistic social contact networks provides two key advantages: (i) they capture the causal processes underlying the epidemic and hence are useful to understand the role of interventions on the course of the epidemics -typically time series models cannot capture this and as a result often do not perform well in such situations; (ii) they provide detailed forecast information -this allows us to produce forecast at high levels of temporal, spatial and social granularity. We also propose a new variation of particle filter technique called beam search particle filtering. The modification allows us to more efficiently search the parameter space which is necessitated by the fact that agentbased techniques are computationally expensive.We illustrate our methodology on the synthetic dataset of Ebola provided as a part of the NSF/NIH Ebola forecasting challenge. Our results show the efficacy of the proposed approach and suggest that agent-based causal models can be combined with filtering techniques to yield a new class of assimilation models for infectious disease forecasting.