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Structured phospholipids with conjugated linolenic acid were produced for potential applications in nutraceuticals and functional foods. Structured phospholipids were synthesized with conjugated linolenic acid (CLnA) from natural sources by catalytic enzymatic reaction. Pomegranate seed oil, as a natural source of CLnA, and an isomerized-concentrated mixture (ICM) of CLnA from flaxseed oil were used for the enzymatic reaction with phosphotidylcholine (PC) using Liposyme TL IM for fatty acid modification at 57 °C for 96 h. The enzymatic process was an effective way to produce structured phospholipids with CLnA. The maximum incorporation of CLnA from pomegranate seed oil and ICM from flaxseed oil into PC was 11.3% and 4.9% after 72 h, respectively. Structured lysophospholipids were also obtained as a result of the enzymatic reaction. The maximum incorporation of CLnA from pomegranate oil and ICM from flaxseed oil into lysophospholipids was 17.2% and 13.5% after 72h, respectively. Physical properties such as dropping point and viscosity at 40 and 50 °C of the structured phospholipids produced were measured when they were added to a chocolate mixture (unsweetened chocolate 94.6%, coconut oil 5% and 0.4 % phospholipids). Two controls were used for comparison: the chocolate mixture without phospholipids and the chocolate mixture with Lipoid S100 (phosphatidylcholine 94%). Structured phospholipids with CLnA showed lower dropping point and viscosities than the controls. Oil-in-water emulsions were prepared with whey protein (1%), soy bean oil (10%) and phospholipids (0.5%) in a high pressure homogenizer at 20MPa. The emulsion stability of the emulsions prepared, control (without phospholipids), Lipoid S 100 and structured phospholipids with CLnA were determined by visual observation of phase separation. The structured phospholipids emulsion showed higher emulsion stability than the controls. This emulsion was stable up to 108 h while the emulsion without phospholipid and Lipoid S100 were 48 h and 96 h stable, respectively. Oxidative stability of the emulsions prepared was determined by measuring the peroxide value and p-anisidine value after 1, 3 and 7 days at 50 °C. Oil was extracted from the emulsions using isooctane:isopropanol (3:2 v/v). The structured phospholipid emulsions showed lower oxidative stability than the controls.

Aim: The purpose of the thesis is to give small private investors an insight the financial world of derivatives and to show that an investor does not need to consult with an advisor in order to make decisions about the investments. The aim was to show through a new product that a small investor can beat the market return. Method: The method used in the thesis is to collect data over a three year period for an option, a bull ETF and a treasury bill. The database DataStream was used to obtain statistics of the option and the Treasury bill and Nasdaq OMX Nordic was used for the Bull ETF. We calculated the expected return and variance of each in order to use in the portfolio. Having the information needed we then used a trial-and-error method to calculate the weight each component will be given, with the help of Excel and its Solver add-on. Result & Conclusion: The results were surprising in that over the three year period the product had a 100% increase, while the market only went up by 30%. The major reason for the products strong return was that the daily earnings were shifted everyday so that the weights remained constant throughout the life of the product. The issue with the product was that no transaction costs were included in the calculations, and as there would be at least one transaction per day the costs would be enormous for the given product. Suggestions for Further Research: As one of the limitations for the thesis was that no transactions cost were included, one idea for further research could be to calculate the transaction costs as well as seeing if there is a method to minimize them so that the product could be profitable. Contribution to the Field: To our knowledge we are the first to test theses three components in order to from a structured product. Through our method interested parties could do the same with other components or retest our product. We have showed through our method one way to create your own structured product.

In the study, young adolescents’ hypothesis development in a computer-supported and problem-based learning environment was examined in terms of two empirical studies. The first study examined the effect of metacognitive scaffolds to strengthening hypothesis development as well as the influence of hypothesis development in the promotion of young adolescents’ problem solving performance in an ill-structured problem solving environment, Animal Investigator. Data was collected from sixth grade students (N = 172). The findings of the study indicated that participants using metacognitive scaffolds attained significantly higher hypothesis-development performance. Results also revealed that the hypothesis-development performance showed the predictive power of the solution development performance. In the second study, the researcher examined three factors, motivation, metacognition, and prior domain knowledge, as a predictor for children’s hypothesisdevelopment performance in the problem-based learning environment. A hypothesized model was evaluated using structural equation modeling, which is a statistical method of causal relationships. Data were collected from sixth grade students (N = 101) in treatment groups. Two significant factors toward children’s hypothesis-development performance in an ill-structured problem solving environment were determined: Prior domain knowledge and metacognition. Implications and limitations of the present study and issues including the experimental design are discussed.

Structured phospholipids with conjugated linolenic acid were produced for potential applications in nutraceuticals and functional foods. Structured phospholipids were synthesized with conjugated linolenic acid (CLnA) from natural sources by catalytic enzymatic reaction. Pomegranate seed oil, as a natural source of CLnA, and an isomerized-concentrated mixture (ICM) of CLnA from flaxseed oil were used for the enzymatic reaction with phosphotidylcholine (PC) using Liposyme TL IM for fatty acid modification at 57 °C for 96 h. The enzymatic process was an effective way to produce structured phospholipids with CLnA. The maximum incorporation of CLnA from pomegranate seed oil and ICM from flaxseed oil into PC was 11.3% and 4.9% after 72 h, respectively. Structured lysophospholipids were also obtained as a result of the enzymatic reaction. The maximum incorporation of CLnA from pomegranate oil and ICM from flaxseed oil into lysophospholipids was 17.2% and 13.5% after 72h, respectively. Physical properties such as dropping point and viscosity at 40 and 50 °C of the structured phospholipids produced were measured when they were added to a chocolate mixture (unsweetened chocolate 94.6%, coconut oil 5% and 0.4 % phospholipids). Two controls were used for comparison: the chocolate mixture without phospholipids and the chocolate mixture with Lipoid S100 (phosphatidylcholine 94%). Structured phospholipids with CLnA showed lower dropping point and viscosities than the controls. Oil-in-water emulsions were prepared with whey protein (1%), soy bean oil (10%) and phospholipids (0.5%) in a high pressure homogenizer at 20MPa. The emulsion stability of the emulsions prepared, control (without phospholipids), Lipoid S 100 and structured phospholipids with CLnA were determined by visual observation of phase separation. The structured phospholipids emulsion showed higher emulsion stability than the controls. This emulsion was stable up to 108 h while the emulsion without phospholipid and Lipoid S100 were 48 h and 96 h stable, respectively. Oxidative stability of the emulsions prepared was determined by measuring the peroxide value and p-anisidine value after 1, 3 and 7 days at 50 °C. Oil was extracted from the emulsions using isooctane:isopropanol (3:2 v/v). The structured phospholipid emulsions showed lower oxidative stability than the controls.

Recently, structured surfaces, consisting of deterministic features designed to produce a particular effect, have shown promise in providing superior functional performance for a range of applications including: low friction surfaces, hydrophobic surfaces and optical effects. Methods have been developed to characterise such structured surfaces. The most widely used characterisation methods are based on segmenting the surface in feature and background regions and then determining the geometrical properties of those features. However, further work is needed to refine these characterisation techniques and provide associated uncertainties. This thesis considers the effect of various segmentation control parameters such as thresholds on the final geometric parameters. The effect of varying filter size is also considered. These considerations should help in selecting a suitable characterisation method for future projects. Additionally, uncertainty in the characterisation should be estimated in order to give an indication of the accuracy of the assessment. However, no previous work has assessed uncertainty in the dimensional properties of structured surfaces. Therefore, this thesis presents two methods to characterise the uncertainty in the geometric characteristics of structured surfaces. First, the measurement reproducibility is used, which can be determined by repeated measurement of a feature. However, measurement reproducibility cannot account for all sources of uncertainty and cannot assess any bias in the measurements. Therefore, a second method based on assessment of the metrological characteristics of the instrument is considered. The metrological characteristics estimate errors produced by the instrument in a way that can easily be measured. Monte Carlo techniques are then used to propagate the effects of the metrological characteristics and their uncertainties into the final measurement uncertainty. For the example used, it was found that the results using the metrological characteristics were in good agreement with the reproducibility results. From these results, it is concluded that the choice of segmentation method, control parameters and filtering can all significantly effect the characterisation of features on a structured surface, often in unexpected ways. Therefore, care must be taken when selecting these values for a specific application. Additionally, two methods of determining the uncertainty of the structured surfaces were considered. Both methods are valid and produce similar results. Using the measurement reproducibility is simple to perform, but requires many measurements and cannot account for some uncertainty sources such as those due to the instrument amplification factors. On the other hand, the use of metrological characteristics can account for all significant sources of uncertainty in a measurement, but is mathematically more complex, requiring Monte Carlo simulations to propagate the uncertainties into the final characteristics. Additionally, other artefacts than the sample being measured are required to determine the metrological characteristics, which may be an issue in some cases.

Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classi cation or regression problems, whose outputs are simple vectors or scalars, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classi cation but has much broader applications, especially in bioinformatics, considering the fact that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-speci c constraints and dependency within the labeling space, the straightforward application of existing deep learning models on the problems can lead to unsatisfactory results. In this dissertation, we argue that the following two ideas can help resolve a wide range of structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design and train problem-speci c deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics sub elds, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D electrical signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve state-of-the-art performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly bene t people's health and wellness. We thus conclude this thesis by discussing such future works, and the potential challenges and opportunities.