Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.
Air traffic control is a demanding task for human operators, as this task requires tracking multiple events, managing the events, and taking actions in the presence of multiple and possibly competing objectives. In such critical tasks, human intelligence is extremely crucial however human decisions also become more prone to errors, which could cause tragic events. One idea to prevent such errors is to design smart machines that can assist human subjects in making decisions whenever human errors become more likely. In this article, we present a simulation model that captures the essence of how a human subject model would interact with a simplified version of an air traffic control simulator, and show how we design a predictor-compensator in order to regulate and possibly improve this interaction, such that overall human-machine interface can be optimized, and human workload is reduced on average.
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