Energy consumption is a major and costly problem in data centers. For many workloads, a large fraction of energy goes to powering idle machines that are not doing any useful work. There are two causes of this inefficiency: low server utilization and a lack of power proportionality. We focus on addressing this problem for two workloads: (1) a traditional, front-end web server workload and (2) an...

Abstract Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers spend more time on other tasks. However, when comes grading exams, fully eliminating work is not yet even with very accurate automated as any mistakes could have significant consequences students. Here, evaluation of an approach therefore extended from measuring relation ...

Mental workload is an important component in complex human-machine systems. The limited applicability of empirical workload measures produces the need for workload modeling and prediction methods. In the present study, a mental workload prediction model is built on the basis of attentional resource allocation and information processing to ensure pilots' accuracy and speed in understanding large...

Background: Firefighting and rescue require high physical and psychological needs under unpredictable and dangerous conditions. Thus, this study aimed to investigate mental workload and affecting factors in firefighters. Materials and Methods: This is a descriptive study which was conducted on 250 firefighters of Tehran, Iran. National Aeronautics and Space Administration Task Load Index (NASA...