In this study, the teeth of the Carcharodon carcharias (Great White) and the Galeocerdo cuvier (Tiger) sharks were analyzed to examine their optimized structure-property relationships and edge serrations with regards to shearing. Structure-property analysis was conducted using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy, X-ray diffraction, and optical microscopy to study the teeth using parametric optimization. Quantifying the structural properties also focused on the tooth serrations, which were captured in SEM and micrographs and were analyzed for geometric parameters using ImageJ software. Nanoindentation was performed to determine the material's mechanical properties. Further, finite element analysis (FEA) of the sharks' teeth serrations were carried out to quantify the optimum shearing performance of each serration type – zeroth (no serrations), first (a single array of serrations), and second (a secondary array of serrations upon the first array) order serration. Here, serration order, bite velocity, and angle-of-impact for ascertaining sharks' teeth shearing performance were analyzed. FEA results showed that serrated edges reduced the energy required to pierce and shear materials as the angle of penetration moved away from perpendicular to the surface. These bioinspired findings will help advance the design and optimization of engineered cutting tools.

Phenomenological material models such as Johnson-Cook plasticity are often used in finite element simulations of large deformation processes at different strain rates and temperatures. Since the material constants that appear in such models depend on the material, experimental data, fitting method, as well as the mathematical representation of strain rate and temperature effects, the predicted material behavior is subject to uncertainty. In this dissertation, evidence theory is used for modeling uncertainty in the material constants, which is represented by separate belief structures that are combined into a joint belief structure and propagated using impact loading simulation of structures. Yager’s rule is used for combining evidence obtained from more than one source. Uncertainty is quantified using belief, plausibility, and plausibility-decision functions. An evidence-based design optimization (EBDO) approach is presented where the nondeterministic response functions are expressed using evidential reasoning. The EBDO approach accommodates field material uncertainty in addition to the embedded uncertainty in the material constants. This approach is applied to EBDO of an externally stiffened circular tube under axial impact load with and without consideration of material field uncertainty caused by spatial variation of material uncertainties due to manufacturing effects. Surrogate models are developed for approximation of structural response functions and uncertainty propagation. The EBDO example problem is solved using genetic algorithms. The uncertainty modeling and EBDO results are presented and discussed.

Det svenska företaget Furhat Robotics har skapat en robot kallad Furhat vilken är kapabel till att interagera med människor i språkcafé-liknande miljöer. Syftet med den robotledda konversationen är att utveckla deltagarnas språkkunskaper, vilka efter varje konversation får svara på en enkät om vad de tyckte om samtalet med Furhat. Ur detta har frågan huruvida det är möjligt att förutspå vad deltagarna tyckte om samtalet baserat på konversationens struktur uppstått. Syftet med denna rapport är att analysera huruvida det är möjligt att kvantifiera konversationerna och förutspå svaren i enkäten med hjälp av maskininlärning. Det dataset som rapporten baserar sig på erhölls från tidigare studier i Kollaborativ Robotassisterad Språkinlärning (Collaborative Robot Assisted Language Learning). Resultaten visade på ett RMSE högre än variansen för medelvärdet av enkätsvaren vilket indikerar att den framtagna modellen inte är särskilt effektiv. Modellen presterade dock bättre i vissa förutsägelser då varje enskilt enkätsvar förutspåddes var för sig. Detta antyder att modellen skulle kunna användas till vissa frågeformuleringar / The Swedish company Furhat Robotic have created a robot called Furhat, which is able to interact with humans in a language café setting. The purpose of the robot led conversation is for the participants to develop their language skills. After the conversation the humans will answer a survey about what they thought about the conversation with Furhat. A question that has arisen from this is if it is possible to predict the survey answers based on just the conversation. The purpose of this paper is to analyze if it is possible to quantify the conversations linked to the survey answers, and by doing so be able to predict the answers in new conversations with a machine learning approach. The data set being used was obtained from an earlier study in Collaborative Robot Assisted Language Learning. The result returned a RMSE that was greater than the variance of the average conversation score which indicates that the model is not very effective. However, it excelled in some predictions trying to give scores to each separate survey answer, indicating that the model could be used for certain question formulations.

People with cerebral palsy (CP) may develop muscle contractures, which are defined as a loss of joint range due to an increase in passive muscle stiffness [1]. Computer models suggest that intramuscular fat can increase muscle stiffness [2]. There is evidence that children with spastic CP have elevated intramuscular fat fractions, but quantitative estimations of the intramuscular fat fraction are variable [1]. Therefore, a reliable method for in vivo quantification of intramuscular fat is required. The aim of this thesis was to compare two- and three-point Dixon fat-water separation methods, as well as the IDEAL algorithm for intramuscular fat quantification of the gastrocnemius medialis, for both children with CP and typically developed peers. As a reference standard, the water- and fat-only maps resulting from the Philips mDIXONXD turbo-spin-echo sequence were used (Ingenia CX, Philips Healthcare, The Netherlands). It was found that neither of the methods can compare to the reference standard, which is probably due to poor performance of the phase unwrapping algorithm applied on this data. Further studies need to be done in order to better quantify the phase error in multi-echo Dixon MRI.

Alfa Laval is a global company that provides solutions, products, and services for industry, such as high-speed separators for marine and food-application. Foulant deposition on the stainless-steel surfaces of Alfa Laval’s high-speed separators has shown to result in lowering of the separation efficiency. In this report, a literature study was conducted to present research documenting the use of analytical techniques for characterization and quantification of oil and dairy foulant deposition on stainless-steel surfaces. The focus of the research was oil foulant deposition and dairy foulant deposition on stainless steel surfaces. The following techniques were examined; NMR, AFM, SEM, XPS/ESCA, EDS/EDX and RBS, ToF – SIMS, EPMA, TGA, Fluorescent Microscopy, and HTGC.  The various techniques were evaluated from their applicability on fouling deposits of Alfa Laval’s high-speed separators and the settings of interest for each technique were also presented. This study also presented possible surface coatings of stainless-steel to impede the initial foulant deposition.  In conclusion, the surface coatings that were found were polyethyleneimine with TiO2 on an epoxy resin surface on the surface, and electronless nickel with embedded polytetrafluoroethylene nanoparticles to reduce surface energy. From the found techniques, the techniques may demonstrate information about specific properties, like chemical composition e.g., and may be suitable for individual use. However, to obtain a general picture of the foulants deposited onto the surface, several of these techniques could be combined, like RBS & EDS. Further research on which techniques are most complementary in identifying, characterizing, and quantifying foulant deposition would be of interest.

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Secretory phospholipase A2 (sPLA2) is an important part of apoptosis and disposal of damaged and dying cells. However, healthy cells are not susceptible to attack by sPLA2. Recent studies have focused on membrane properties necessary to induce susceptibility in both artificial and biological membranes. Hydrolysis of phospholipids by sPLA2 requires at least two preliminary steps: first, adsorption of the enzyme to the cellular membrane, and second, movement of a phospholipid into the active site of the enzyme. We determined the effects of susceptibility on each of the two steps and determined the contributions changing the equilibrium constants have on susceptibility. The equilibrium constant for step one increased by a factor of 2 during susceptibility, while the equilibrium constant for step two increased by a factor of 4. The rise in the second equilibrium constant caused the majority of the change in hydrolysis rate seen during susceptibility; the influence of the first equilibrium constant is minimal. We confirmed these results with adsorption studies (assessment of the first step). We additionally found that sPLA2 has a high affinity for the cellular membrane and that only a small percentage (3-5%) of the membrane is covered when all adsorption sites are filled by the enzyme. We proposed a mathematical model describing the mechanism of action of sPLA2, and we were able to experimentally justify the assumptions made in the model.