Optimal inter-object correlation when replicating for availability

10.1007/s00446-008-0072-2Distributed Computing215367-384DICO

National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand : Australian clinical guidelines for the management of heart failure 2018

INTRODUCTION: Heart failure (HF) is a clinical syndrome that is secondary to an abnormality of cardiac structure or function. These clinical practice guidelines focus on the diagnosis and management of HF with recommendations that have been graded on the strength of evidence and the likely absolute benefit versus harm. Additional considerations are presented as practice points. Main recommendations: Blood pressure and lipid lowering decrease the risk of developing HF. Sodium-glucose cotransporter 2 inhibitors decrease the risk of HF hospitalisation in patients with type 2 diabetes and cardiovascular disease. An echocardiogram is recommended if HF is suspected or newly diagnosed. If an echocardiogram cannot be arranged in a timely fashion, measurement of plasma B-type natriuretic peptides improves diagnostic accuracy. Angiotensin-converting enzyme inhibitors, β-blockers and mineralocorticoid receptor antagonists improve outcomes in patients with HF associated with a reduced left ventricular ejection fraction. Additional treatment options in selected patients with persistent HF associated with reduced left ventricular ejection fraction include switching the angiotensin-converting enzyme inhibitor to an angiotensin receptor neprilysin inhibitor; ivabradine; implantable cardioverter defibrillators; cardiac resynchronisation therapy; and atrial fibrillation ablation. Multidisciplinary HF disease management facilitates the implementation of evidence-based HF therapies. Clinicians should also consider models of care that optimise medication titration (eg, nurse-led titration). Changes in management as a result of the guideline: These guidelines have been designed to facilitate the systematic integration of recommendations into HF care. This should include ongoing audit and feedback systems integrated into work practices in order to improve the quality of care and outcomes of patients with HF

Improving Industrial Solar Cell Fabrication using Neural Networks and Genetic Algorithms

The present thesis approaches the improvement of the performance metrics of industrially fabricated solar cells. It aims to develop an optimization method that uses machine learning to identify an improved configuration of solar cell production lines. Conventional methods for process improvement rely on modelling experimental data to improve a process of interest. The number of experiments needed for modelling grows exponentially with the number of parameters taken into account. Since experiments are costly and time-consuming, the number of parameters that can be considered in this existing method is limited. This existing method thus allows modelling of only a few processes, and cannot include all inter-dependencies between the processes in the production line. In this thesis, an alternative method is proposed for the optimization of solar cell fabrication that uses machine learning and genetic algorithms. Instead of collecting experimental data, this method uses data collected from sensors in the production line. Machine learning is employed to establish a model of the production using the natural variation in this sensor data. This allows us to create more complete and accurate models of the production line. Genetic algorithms are then combined with the machine learning model to identify an improved configuration of process parameters. As part of this study, a virtual solar cell production line was developed to test and compare different machine learning algorithms. Based on experiments with this virtual production line, two novel approaches are proposed that can optimize production lines using a combination of artificial neural networks and genetic algorithms. It was demonstrated that the first approach improved the photovoltaic cell efficiency of cells from a simulated production line from 17.9$\pm0.3\%$ to 19.2$\pm0.2\%$. This significant improvement demonstrates the power of applying machine learning to solar cell production. The results achieved in this thesis encourage further development of the proposed method and application to real-life production lines