Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem.

The Covering Location Problem (CLP) is widely used for the efficient facility distribution. However, existing algorithms for this problem suffer from long computation times or suboptimal solutions. To address this, we propose two methods based on graph convolutional networks (GCN) to solve two types of covering location problems: the location set covering problem and the maximum covering location problem.

Sleep deprivation and a non-24-h working schedule lead to extensive alterations in physiology and behavior.

Most organisms on Earth possess circadian rhythms in their physiology and behaviors that allow them to resonate with the cycling environment over a 24-h period. However, in human society, a substantial quantity of jobs requires non-24-h working and rest or shift schedules, which causes more or less misalignment in circadian rhythms and disorders as a consequence. In this work, we conducted a sleep deprivation (SD) and non-24-h working and rest schedule (8 h on and 4 h off) experiment over 10 d in total and measured the changes in a series of physiologic and cognitive parameters.