Micrometre-scale plasticity size effects in metals and ceramics: theory and experiment.

PhDThis thesis comprises studies of size effects in the plasticity of metals and ceramics at length scales of the order of micrometres and includes both experimental work and theoretical development. Experimental results are presented for foil flexure (nickel and copper)and nanoindentation (ceramics and hard metals).These studies were conducted because existing data does not cover a range broad enough or with sufficient precision to test various theories. With the developed bending technique more accurate data is obtained covering a wide range of strain, especially around the key region of the elastic-plastic transition. Moreover, the interaction between grain and thickness size effect is successfully studied by varying the ratio of grain size over thickness of the foils. After carefully calibrating the indenters, the macroscopic indentation yield strength for ceramics and high strength metals is determined in a direct way by using spherical nanoindentation. The magnitude of size effect is significantly different between metals and ceramics. By comparing the Berkovich and spherical indentation size effect, the results implies that the contact size, a, is the most fundamental length scale in the indentation size effect, independent of the indenter shape. The indentation strength is found to be inversely scaled with the square root of a. The slip-distance theory (based on (Conrad et al, 1967)) with an effective length scale reconciling intrinsic and extrinsic size effects appears able to account for the size effects in all contexts, without requiring strain gradient plasticity theory or an implicit characteristic length

Fusing Continuous-valued Medical Labels using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78$\pm$0.63ms, significantly outperforming the best Challenge entry (15.37$\pm$2.13ms) as well as the EM, mean, and median voting strategies (14.76$\pm$0.52ms, 17.61$\pm$0.55ms, and 14.43$\pm$0.57ms respectively with $p<0.0001$)

The Performance of Canadian Pooled Equity Funds

In this paper, we evaluate and rank the performance of 65 Canadian Equity Pooled Funds. We adopt traditional performance measures to evaluate pooled fund managers’ performances from January 1999 to December 2008. We employ the geometric mean as a reward measure, standard deviation and beta coefficient as risk measures, and Capital Asset Pricing Model (CAPM) risk-adjusted measures that include Jensen’s (1968) alpha, the Treynor (1965) ratio, the Sharpe (1966) ratio, and Modigliani and Modigliani’s (1997) M-Squared. Treynor-Mazuy (1966) and Henriksson-Merton (1981) are used to measure market-timing. According to our results, thirty-five percent of 65 Canadian Equity Pooled Funds managers have abnormal returns in terms of Jensen’s (1968) alpha. Only eight pooled fund managers have market-timing ability. None of 65 pooled fund managers has both selectivity and market-timing ability at the same time

Performance of boilers equipped with vapor-pump (BEVP) system equipped with a novel air-flue gas total heat exchanger

Because of high humidity and nonlinearity of flue gas, waste heat from flue gas is hard to recovery. Boilers equipped with vapor-pump system is developed to solve the problem caused by high humidity. In this system, double spray towers subsystem is equipped to realize total heat waste heat recovery. However, caused by nonlinearity, limited waste heat recovery efficiency is just 83 % (1 segment) and 93 % (2 segment). Further, based on boilers equipped with vapor-pump (BEVP) system, enthalpy wheel system is developed to solve the problem caused by nonlinearity. However, enthalpy wheel system cannot solve the problem completely. In this article, a novel air-flue gas total heat exchanger is put forward to achieve full waste heat recovery. In this system, waste heat recovery efficiency limit is up to 100 %. Then, the limit condition of total heat transfer process is discussed. Performance of the total heat exchanger is discussed and compared to double spray towers system and enthalpy wheel system. As the result, considering heat transfer temperature difference, the total heat exchanger total heat transfer efficiency of the total heat exchanger is 7 % higher than 2-segment BEVP system and 10 % higher than enthalpy wheel system.</p

DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU

Survival analysis helps approximate underlying distributions of time-to-events which in the case of critical care like in the ICU can be a powerful tool for dynamic mortality risk prediction. Extending beyond the classical Cox model, deep learning techniques have been leveraged over the last years relaxing the many constraints of their counterparts from statistical methods. In this work, we propose a novel conditional variational autoencoder-based method called DySurv which uses a combination of static and time-series measurements from patient electronic health records in estimating risk of death dynamically in the ICU. DySurv has been tested on standard benchmarks where it outperforms most existing methods including other deep learning methods and we evaluate it on a real-world patient database from MIMIC-IV. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets supporting the idea that dynamic deep learning models based on conditional variational inference in multi-task cases can be robust models for survival analysis