Collecting and analyzing patients' e-healthcare data in Medical Internet-of-Things (MIOT), e-Healthcare providers can offer reliable medical services that will achieve better treatment for patients. For example, the diagnosis of disease and predictions of health offer an alternative and helpful evaluation of the risk of diseases, thereby helping patients lead a healthier life. However, e-Healthcare providers cannot cope with the huge volumes of data and respond to this online service such that a feasible solution is adopted to outsource the medical data to powerful medical cloud servers. Since medical data are very sensitive and outsourced servers are not fully trusted, a direct outsourcing decision tree evaluation service will inevitably result in huge privacy risks with regards to patient identity or original medical data. It is hard to hide the results of an evaluation from the single-server model unless a fully homomorphic cryptosystem is used, or the requester must communicat
Operational disturbances within 30 min, namely, short-term operational disturbances (STODs), occur frequently during the daily operation of an urban rail transit (URT) system. Therefore, there is an urgent need to assess a network’s ability to respond to STODs to improve its operational level. The resilience of a URT network jointly considering turn-back operations, the occurrence time of STODs, and passenger travel experience is addressed. By considering passenger travel alternatives under STODs, we assessed the resilience of a network by utilizing the ratio of the average loss of a time-dependent performance indicator, which is referred to as the operational service level indicator in this paper. A simulation-based resilience assessment flowchart is also proposed. Numerical experiments conducted on the Chengdu subway network indicate that this network is more resilient to successive station failure than to simultaneous failure. Guaranteeing the normal operation of transfer stations
Andre Khalil’s Computational Modeling, Analysis of Imagery and Numerical Experiments (CompuMAINE) lab has used image analysis techniques to solve a range of problems at every scale, from cancer cells to galaxies. And he has only scratched the surface of its potential. Since 2008, over $1 million in funding has supported CompuMAINE research using image analysis […]