2 resultados para 1 sigma standard deviation for the average

em Glasgow Theses Service


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The clinical syndrome of heart failure is one of the leading causes of hospitalisation and mortality in older adults. Due to ageing of the general population and improved survival from cardiac disease the prevalence of heart failure is rising. Despite the fact that the majority of patients with heart failure are aged over 65 years old, many with multiple co-morbidities, the association between cognitive impairment and heart failure has received relatively little research interest compared to other aspects of cardiac disease. The presence of concomitant cognitive impairment has implications for the management of patients with heart failure in the community. There are many evidence based pharmacological therapies used in heart failure management which obviously rely on patient education regarding compliance. Also central to the treatment of heart failure is patient self-monitoring for signs indicative of clinical deterioration which may prompt them to seek medical assistance or initiate a therapeutic intervention e.g. taking additional diuretic. Adherence and self-management may be jeopardised by cognitive impairment. Formal diagnosis of cognitive impairment requires evidence of abnormalities on neuropsychological testing (typically a result ≥1.5 standard deviation below the age-standardised mean) in at least one cognitive domain. Cognitive impairment is associated with an increased risk of dementia and people with mild cognitive impairment develop dementia at a rate of 10-15% per year, compared with a rate of 1-2% per year in healthy controls.1 Cognitive impairment has been reported in a variety of cardiovascular disorders. It is well documented among patients with hypertension, atrial fibrillation and coronary artery disease, especially after coronary artery bypass grafting. This background is relevant to the study of patients with heart failure as many, if not most, have a history of one or more of these co-morbidities. A systematic review of the literature to date has shown a wide variation in the reported prevalence of cognitive impairment in heart failure. This range in variation probably reflects small study sample sizes, differences in the heart failure populations studied (inpatients versus outpatients), neuropsychological tests employed and threshold values used to define cognitive impairment. The main aim of this study was to identify the prevalence of cognitive impairment in a representative sample of heart failure patients and to examine whether this association was due to heart failure per se rather than the common cardiovascular co-morbidities that often accompany it such as atherosclerosis and atrial fibrillation. Of the 817 potential participants screened, 344 were included in this study. The study cohort included 196 patients with HF, 61 patients with ischaemic heart disease and no HF and 87 healthy control participants. The HF cohort consisted of 70 patients with HF and coronary artery disease in sinus rhythm, 51 patients with no coronary artery disease in sinus rhythm and 75 patients with HF and atrial fibrillation. All patients with HF had evidence of HF-REF with a LVEF <45% on transthoracic echocardiography. The majority of the cohort was male and elderly. HF patients with AF were more likely to have multiple co-morbidities. Patients recruited from cardiac rehabilitation clinics had proven coronary artery disease, no clinical HF and a LVEF >55%. The ischaemic heart disease group were relatively well matched to healthy controls who had no previous diagnosis of any chronic illness, prescribed no regular medication and also had a LVEF >55%. All participants underwent the same baseline investigations and there were no obvious differences in baseline demographics between each of the cohorts. All 344 participants attended for 2 study visits. Baseline investigations including physiological measurements, electrocardiography, echocardiography and laboratory testing were all completed at the initial screening visit. Participants were then invited to attend their second study visit within 10 days of the screening visit. 342 participants completed all neuropsychological assessments (2 participants failed to complete 1 questionnaire). A full comprehensive battery of neuropsychological assessment tools were administered in the 90 minute study visit. These included three global cognitive screening assessment tools (mini mental state examination, Montreal cognitive assessment tool and the repeatable battery for the assessment of neuropsychological status) and additional measures of executive function (an area we believe has been understudied to date). In total there were 9 cognitive tests performed. These were generally well tolerated. Data were also collected using quality of life questionnaires and health status measures. In addition to this, carers of the study participant were asked to complete a measure of caregiver strain and an informant questionnaire on cognitive decline. The prevalence of cognitive impairment varied significantly depending on the neuropsychological assessment tool used and cut-off value used to define cognitive impairment. Despite this, all assessment tools showed the same pattern of results with those patients with heart failure and atrial fibrillation having poorer cognitive performance than those with heart failure in sinus rhythm. Cognitive impairment was also more common in patients with cardiac disease (either coronary artery disease or heart failure) than age-, sex- and education-matched healthy controls, even after adjustment for common vascular risk factors.

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Considerable interest in renewable energy has increased in recent years due to the concerns raised over the environmental impact of conventional energy sources and their price volatility. In particular, wind power has enjoyed a dramatic global growth in installed capacity over the past few decades. Nowadays, the advancement of wind turbine industry represents a challenge for several engineering areas, including materials science, computer science, aerodynamics, analytical design and analysis methods, testing and monitoring, and power electronics. In particular, the technological improvement of wind turbines is currently tied to the use of advanced design methodologies, allowing the designers to develop new and more efficient design concepts. Integrating mathematical optimization techniques into the multidisciplinary design of wind turbines constitutes a promising way to enhance the profitability of these devices. In the literature, wind turbine design optimization is typically performed deterministically. Deterministic optimizations do not consider any degree of randomness affecting the inputs of the system under consideration, and result, therefore, in an unique set of outputs. However, given the stochastic nature of the wind and the uncertainties associated, for instance, with wind turbine operating conditions or geometric tolerances, deterministically optimized designs may be inefficient. Therefore, one of the ways to further improve the design of modern wind turbines is to take into account the aforementioned sources of uncertainty in the optimization process, achieving robust configurations with minimal performance sensitivity to factors causing variability. The research work presented in this thesis deals with the development of a novel integrated multidisciplinary design framework for the robust aeroservoelastic design optimization of multi-megawatt horizontal axis wind turbine (HAWT) rotors, accounting for the stochastic variability related to the input variables. The design system is based on a multidisciplinary analysis module integrating several simulations tools needed to characterize the aeroservoelastic behavior of wind turbines, and determine their economical performance by means of the levelized cost of energy (LCOE). The reported design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity. The presented technology is applied to the design of a 5-MW HAWT rotor to be used at sites of wind power density class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing the mean and standard deviation of the LCOE. Airfoil shapes, spanwise distributions of blade chord and twist, internal structural layup and rotor speed are optimized concurrently, subject to an extensive set of structural and aeroelastic constraints. The effectiveness of the multidisciplinary and robust design framework is demonstrated by showing that the probabilistically designed turbine achieves more favorable probabilistic performance than those of the initial baseline turbine and a turbine designed deterministically.