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Robot-mediated rehabilitation is a rapidly advancing discipline that seeks to develop improved treatment procedures using new technologies, e.g., robotics, coupled with modern theories in neuroscience and rehabilitation. A robotic device was designed and developed for rehabilitation of upper limbs of post stroke patients. A novel force feedback bimanual working mode provided real-time dynamic sensation of the paretic hand. Results of the preliminary clinical tests revealed a quantitative evaluation of the patient's level of paresis and disability.

Recently, resilience studies have become an indispensable tool for sustainable operation of energy infrastructure. In line with the need, this study presents a mathematical model to enhance resilience level of power distribution systems against natural disasters. The model is designed as a three-stage algorithm according to system operators' actions. The first stage schedules pre-event actions. At this stage, forecasts about the approaching disaster as well as fragility curves of system components are used to identify failure probability of system components. The failure probabilities are used to trip out the lines as much as possible to defensively operate the distribution network, and advantages of alternatives such as distributed energy resources and normally-open switches are taken to serve critical loads. The second stage is to monitor system operating conditions during the event and identify the status of system components. The third stage mainly focuses on scheduling postevent actions. At this stage, based on real data about different elements of the network, available alternatives are taken to restore as much critical load as possible. To evaluate performance of the model, it is applied to a distribution test system and the results are discussed in detail.

Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault isolation and system reconfiguration, is contemplated to place tie-lines in the most proper locations to enhance the manoeuvring capability of distribution systems and reducing the customer interruption time. Furthermore, the model is a stochastic one wherein uncertainty associated with potential damages alongside demand uncertainty is captured via a set of likely scenarios. To validate the effectiveness of the proposed stochastic mixed-integer linear programming model, it is tested and verified on the IEEE 13-and 123-bus test systems.