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This paper discusses a novel method for modeling the spread of an epidemic that facilitates the calculation of the optimal control policy. The proposed model considers seven compartments in the population as opposed to popular approaches based on three or four compartments. The usual compartments, i.e., susceptible, exposed, infected, and recovered individuals have been included in the seven compartment model with the addition of compartments pertaining to individuals under treatment, vaccinated individuals, and individuals in quarantine. The addition of new compartments allows for the incorporation of multiple control options such as treatment, quarantine, and vaccination. The mathematical expressions involved in the model have been described followed by a discussion on the calculation of optimal control policy. Finally, a simulation-based example has been included for demonstrating the effectiveness of the control policy resulting from the proposed model.

This paper describes a modeling method for predicting a human's task-level intent through the use of Markov Decision Processes. Intent prediction can be used by a robot to improve decision-making when human and robot operate in a shared physical space. This work presumes human and robot goals are independent such that the robot seeks to avoid interfering with the human rather than directly assisting the human. The proposed human intent prediction system transforms goal sequences the human is expected to complete, a limited past action history, and a correlation of observed behaviors with actions into a prediction of the in-progress or next action the humans is most likely to take. An intra-vehicle activity space robotics application example is presented.

Graphene nanosheets were exfoliated from graphite using liquid exfoliation method. Smart sensing layer was prepared by dispersing graphene nanosheets in thermoplastic polyurethane. The smart sensing layers thus obtained were pasted on to the glass fiber laminated composite specimens. The sensing layer due to its piezoresistivity was employed for detecting strains in the composite specimens. The results show that the smart sensing layer can be employed for strain sensing in the composite structures. The results hold promise for various applications of these sensors for structural health monitoring in composite parts.

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This paper describes a modeling method for predicting a human's task-level intent through the use of Markov Decision Processes. Intent prediction can be used by a robot to improve decision-making when human and robot operate in a shared physical space. This work presumes human and robot goals are independent such that the robot seeks to avoid interfering with the human rather than directly assisting the human. The proposed human intent prediction system transforms goal sequences the human is expected to complete, a limited past action history, and a correlation of observed behaviors with actions into a prediction of the in-progress or next action the humans is most likely to take. An intra-vehicle activity space robotics application example is presented.

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