Accurate predictions of the annual energy yield from wave energy converters are essential to the development of the wave industry. The current method based on power matrices uses only a small part of the data available from sea state estimations and it is consequently prone to inaccuracies. The research presented in this work investigates the issue of energy yield prediction and questions the power matrix method. This is accomplished by quantifying the influence of several directional sea states parameters on the performances of wave energy converters. The approach taken was to test several wave energy converters in the Edinburgh Curved tank with a large set of sea states. The selected wave energy converters are a fix OWC, a set of two OWCs acting as a weak directional device and the desalination duck model. Uni-modal and bi-modal sea states were used. For the uni-modal sea states, parameters related to the wave system shape were considered. For the bi-modal sea states, the relative position of the wave system peaks was investigated and the uni-modality index was introduced to quantify the degree to which sea states could be considered bi-modal. For all sea states, the significant wave height was kept constant. The experimental work required good spectral estimates. The MLM and MMLM were adapted to deterministic waves to improve their stability and accuracy. A routine to isolate wave systems was also developed in order to estimate parameters with respect to each wave systems. For uni-modal spectra, parametric models of the observed performances of the devices could be devised. The frequency spreading and its interaction with the energy period proved to be as important as the energy period itself, which suggests that the frequency spreading should be used for energy production prediction. For bi-modal spectra, evidence of the duck sensitivity to directionality was found while the OWCs were not affected.

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Removable Epoxy Foam (REF) and other rigid foams experience severe changes in structure and properties when exposed to high heat. As thermal energy breaks network bonds in the foam many species are formed, including large polymer-like network fragments and smaller solvent-like molecules. During this process a liquid phase may form. The vapor-liquid equilibrium (VLE) behavior of the polymer solutions formed during initial decomposition can be highly non-ideal. In this research VLE behavior of high-temperature polymer solutions was studied and a procedure was developed for predicting that behavior during decomposition of rigid foams. A high-temperature VLE facility was built and validated, and equilibrium pressures were measured at temperatures between 75 and 250ºC for six polymer/solvent systems: two polymers – polyethylene glycol and polystyrene – with each of three solvents – benzene, furan, and 4-isopropylphenol. Calculations from eighteen polymer solution models were compared with experimental results to determine which model best described the VLE behavior. These models included six existing activity coefficient models used alone, as well as in combinations with the Peng-Robinson equation of state (EOS) through the Wong-Sandler mixing rules. Because several of the models required values for polymer volumes, a comparison of the GCVOL and GCMCM group-contribution volume estimation methods was performed. GCMCM was found to give lower overall deviations from literature polymer volume data. The models involving an equation of state required EOS parameter values for the pure polymers. A new method for determining these parameters was proposed. Models using parameters from the new method gave better agreement with equilibrium pressure data than models using parameters from the recommended method in the literature. While agreement with equilibrium pressure data was similar for several models, some models predicted a liquid phase split under certain conditions. Data were not available to verify the presence of two liquid phases, but are needed to make an appropriate recommendation of the best model. If liquid phase splitting does not occur, it is recommended that the UNIFAC-ZM activity coefficient model be used alone. If phase splitting behavior is observed, it is recommended that the UNIFAC-FV activity coefficient model be used in combination with the Peng-Robinson EOS.

Mental health is essential for overall well-being, focusing emotional, psychological, and social aspects. Assessing and managing mental health requires understanding mental state parameters, including cognitive load, cognitive impairment, and emotional state. Advanced technologies like eye tracking provide valuable insights into these parameters, transformed mental health evaluation and enabled more targeted interventions and better outcomes. Thesis focused towards developing intelligent system to monitor mental health, focusing on cognitive load, cognitive impairment, and emotional state. The research has three main objectives, including creating four eye-tracking-based unimodal datasets and a multimodal dataset to address the lack of publicly available mental health assessment datasets. Each dataset is designed to study cognitive load, cognitive impairment, and emotional state classification using varied stimuli. In addition to dataset creation, the thesis excels in feature extraction, introducing novel features to detect mental state parameters and enhancing assessment precision. High-level features such as error rate, scanpath comparison score, and inattentional blindness are incorporated, contributing to find cognitive impairment scores. Five models are developed to detect mental states by separately monitoring the mental state parameters, cognitive load, cognitive impairment, and emotional state. The models employ statistical analysis, machine learning algorithms, fuzzy inference systems, and deep learning techniques to provide detailed insights into an individual's mental state. The first two models, Eye-Tracking Cognitive Load models (ECL-1 and ECL-2) focus on cognitive load assessment during mathematical assessments and Trail Making Test tasks. ECL-1 model utilizes statistical analysis to understand the correlation between eye tracking features like pupil diameter and blink frequency with the cognitive load while performing mathematical assessments. With the identification of relevant features while performing Trail Making Test (TMT), the ECL-2 model effectively classifies low and high cognitive load states with a notable 94% accuracy, utilizing eye-tracking data and machine learning algorithms. The third model, the ETMT (Eye tracking based Trail Making Test) model, uses a fuzzy inference system and adaptive neuro-fuzzy inference system to detect mental states associated with cognitive impairment. It provides detailed scores in visual search speed and focused attention, important for understanding the exact cognitive deficits of a patient. This greatly aids in understanding the cognitive states of an individual and addresses deficits in executive functioning, memory, motor function, attentional disengagement, neuropsychological function, processing speed, and visual attention. The fourth model, PredictEYE, utilizes a deep learning time-series univariate regression model based on Long Short-Term Memory (LSTM) to predict future sequences of each feature. Machine learning-based Random Forest algorithm is applied on the predicted features for mental state prediction and identifying the mental state as calm or stressful based on a person's emotional state. The personalized time series methodology makes use of the power of time series analysis, identifying patterns and changes in data over time to enable more precise and individualized mental health assessments and monitoring. Notably, PredictEYE outperforms ARIMA with an accuracy of 86.4%. The fifth model introduced in this study is based on a multimodal dataset, incorporating physiological measures such as ECG, GSR, PPG, and respiratory signals, along with eye tracking data. Two separate models, one based on eye tracking data and the other based on all other physiological measures developed for understanding the emotional state of a person. These models demonstrate comparable performance, with notable proficiency in binary classification based on arousal and valence. Particularly, the Binary-Valence model achieves slightly higher accuracy when utilizing eye tracking data, while other physiological measures exhibit stronger classification performance for the Binary-Arousal model. The thesis makes substantial progress in mental health monitoring by providing accurate, non-intrusive evaluations of an individual's mental state. It emphasizes mental state parameters such as cognitive load, impairment, and emotional state, with AI-based methods incorporated to improve the precision in detection of mental state.