Background: Although sleep problems in autistic teenagers are associated with impaired daytime functioning, it remains unclear how sleep and daytime functioning are related. Method: We used a network approach to disentangle patterns between sleep, sleep hygiene, and daytime functioning. Over a three-week period, 31 autistic teenagers answered daily questions about sleep and daytime functioning. Sleep tracker data were collected from 14 of the teenagers. We preregistered the analysis plan for this study at AsPredicted (#34594; https://aspredicted. org/blind.php?x = 3c4t65). Results: Perceived sleep quality seemed to be the most important sleep variable in relation to daytime functioning (self/parent/teacher reports). We also found that sleep onset latency, total sleep time, and wake time after sleep onset were related to daytime functioning, but mostly indirectly through perceived sleep quality. Conclusion: These findings are important for developing sleep interventions because perceived sleep quality would be a logical target for increasing the likelihood of actually improving daytime functioning.
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
