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Background Are thermoregulation and golden hour practices in extremely preterm (EP) infants comparable across the world? This study aims to describe these practices for EP infants based on the neonatal intensive care unit’s (NICUs) geographic region, country’s income status and the lowest gestational age (GA) of infants resuscitated. Methods The Director of each NICU was requested to complete the e-questionnaire between February 2019 and August 2021. Results We received 848 responses, from all geographic regions and resource settings. Variations in most thermoregulation and golden hour practices were observed. Using a polyethylene plastic wrap, commencing humidity within 60 min of admission, and having local protocols were the most consistent practices (>75%). The odds for the following practices differed in NICUs resuscitating infants from 22 to 23 weeks GA compared to those resuscitating from 24 to 25 weeks: respiratory support during resuscitation and transport, use of polyethylene plastic wrap and servo-control mode, commencing ambient humidity >80% and presence of local protocols. Conclusion Evidence-based practices on thermoregulation and golden hour stabilisation differed based on the unit’s region, country’s income status and the lowest GA of infants resuscitated. Future efforts should address reducing variation in practice and aligning practices with international guidelines. Impact A wide variation in thermoregulation and golden hour practices exists depending on the income status, geographic region and lowest gestation age of infants resuscitated. Using a polyethylene plastic wrap, commencing humidity within 60 min of admission and having local protocols were the most consistent practices. This study provides a comprehensive description of thermoregulation and golden hour practices to allow a global comparison in the delivery of best evidence-based practice. The findings of this survey highlight a need for reducing variation in practice and aligning practices with international guidelines for a comparable health care delivery.

Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive to the system states in closed-loop power system dynamics. Extending the recently developed mean square exponential stability (MSES) results for stochastic systems, we formulate multi-objective linear matrix inequality (LMI)-based optimal control synthesis problems to not only guarantee stochastic stability, but also promote sparsity, enhance closed-loop transient performance, and maximize allowable uncertainties. The fundamental trade-off between the maximum allowable (\textit{critical}) uncertainty levels and the optimal stochastic stabilizing control efforts is established. Moreover, the sparse control synthesis problem is generalized to the realistic power systems scenario in which only partial-state measurements are available. Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts., accepted for publication at the IEEE Transactions on Power Sysems

Background Globally, are skincare practices and skin injuries in extremely preterm infants comparable? This study describes skin injuries, variation in skincare practices and investigates any association between them. Methods A web-based survey was conducted between February 2019 and August 2021. Quantifying skin injuries and describing skincare practices in extremely preterm infants were the main outcomes. The association between skin injuries and skincare practices was established using binary multivariable logistic regression adjusted for regions. Results Responses from 848 neonatal intensive care units, representing all geographic regions and income status groups were received. Diaper dermatitis (331/840, 39%) and medical adhesive-related skin injuries (319/838, 38%) were the most common injuries. Following a local skincare guideline reduced skin injuries [medical adhesive-related injuries: adjusted odds ratios (aOR) = 0.63, 95% confidence interval (CI) = 0.45–0.88; perineal injuries: aOR = 0.66, 95% CI = 0.45–0.96; local skin infections: OR = 0.41, 95% CI = 0.26–0.65; chemical burns: OR = 0.46, 95% CI = 0.26–0.83; thermal burns: OR = 0.51, 95% CI = 0.27–0.96]. Performing skin assessments at least every four hours reduced skin injuries (abrasion: aOR = 0.48, 95% CI = 0.33–0.67; pressure: aOR = 0.51, 95% CI = 0.34–0.78; diaper dermatitis: aOR = 0.71, 95% CI = 0.51–0.99; perineal: aOR = 0.52, 95% CI = 0.36–0.75). Regional and resource settings-based variations in skin injuries and skincare practices were observed. Conclusions Skin injuries were common in extremely preterm infants. Consistency in practice and improved surveillance appears to reduce the occurrence of these injuries. Better evidence regarding optimal practices is needed to reduce skin injuries and minimize practice variations.

This paper presents an information-theoretic approach for model reduction for finite time simulation. Although system models are typically used for simulation over a finite time, most of the metrics (and pseudo-metrics) used for model accuracy assessment consider asymptotic behavior e.g., Hankel singular values and Kullback-Leibler(KL) rate metric. These metrics could further be used for model order reduction. Hence, in this paper, we propose a generalization of KL divergence-based metric called n-step KL rate metric, which could be used to compare models over a finite time horizon. We then demonstrate that the asymptotic metrics for comparing dynamical systems may not accurately assess the model prediction uncertainties over a finite time horizon. Motivated by this finite time analysis, we propose a new pragmatic approach to compute the influence of a subset of states on a combination of states called information transfer (IT). Model reduction typically involves the removal or truncation of states. IT combines the concepts from the n-step KL rate metric and model reduction. Finally, we demonstrate the application of information transfer for model reduction. Although the analysis and definitions presented in this paper assume linear systems, they can be extended for nonlinear systems.