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| http://dx.doi.org/10.4300/JGME-D-18-01010 | DOI Listing |
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Theory-informed refinement and tailored implementation of a quality improvement program in maternity care to reduce unwarranted clinical variation across a health service network.
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Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
Background: Unwarranted clinical variation presents a major challenge in contemporary healthcare, indicating potential inequalities and inefficiencies, and unrealised potential for better outcomes. Despite an increasing focus on unwarranted clinical variation, and consideration of efforts to address this challenge, evidence-based strategies which achieve this are limited. Audit and feedback of healthcare processes (process auditing) and clinician engagement are important tools which may help to reduce unwarranted clinical variation, however their application in maternity care is yet to be thoroughly explored.
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Department of Wildlife Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, 39762-9690, USA.
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In the past several decades, much attention has been focused on the effects of dispersal on total populations of species. In Zhang (EL 20:1118-1128, 2017), a rigorous biological experiment was performed to confirm the mathematical conclusion: Dispersal tends to enhance populations under a suitable hypothesis. In addition, mathematical models keeping track of resource dynamics in population growth were also proposed in Zhang (EL 20:1118-1128, 2017) to understand this remarkable phenomenon.
View Article and Find Full Text PDFControlling the energies of the single-rotor large wind turbine system using a new controller.
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Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, KSA, Saudi Arabia.
In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators.
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