7 research outputs found
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Integrated computer-aided working-fluid design and thermoeconomic ORC system optimisation
The successful commercialisation of organic Rankine cycle (ORC) systems across a range of power outputs and heat-source temperatures demands step-changes in both improved thermodynamic performance and reduced investment costs. The former can be achieved through high-performance components and optimised system architectures operating with novel working-fluids, whilst the latter requires careful component-technology selection, economies of scale, learning curves and a proper selection of materials and cycle configurations. In this context, thermoeconomic optimisation of the whole power-system should be completed aimed at maximising profitability. This paper couples the computer-aided molecular design (CAMD) of the working-fluid with ORC thermodynamic models, including recuperated and other alternative (e.g., partial evaporation or trilateral) cycles, and a thermoeconomic system assessment. The developed CAMD-ORC framework integrates an advanced molecular-based group-contribution equation of state, SAFT-γ Mie, with a thermodynamic description of the system, and is capable of simultaneously optimising the working-fluid structure, and the thermodynamic system. The advantage of the proposed CAMD-ORC methodology is that it removes subjective and pre-emptive screening criteria that would otherwise exist in conventional working-fluid selection studies. The framework is used to optimise hydrocarbon working-fluids for three different heat sources (150, 250 and 350 °C, each with mcp = 4.2 kW/K). In each case, the optimal combination of working-fluid and ORC system architecture is identified, and system investment costs are evaluated through component sizing models. It is observed that optimal working fluids that minimise the specific investment cost (SIC) are not the same as those that maximise power output. For the three heat sources the optimal working-fluids that minimise the SIC are isobutane, 2-pentene and 2-heptene, with SICs of 4.03, 2.22 and 1.84 £/W respectively
Subcortical volumes across the lifespan: data from 18,605 healthy individuals aged 3-90 years
Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.Education and Child Studie
A review of patient outcomes in pharmacological studies from the psychiatric literature, 1966â1993
Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes
Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years
Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability