The aim of the diploma thesis was to distinguish bird species based on the expected different protein composition of eggs and their individual components using mass spectrometry. The obtained results could be possibly used to identify the animal origin of egg materials used in works of art. For the research, components of the eggs (yolk, egg white and their mixture) of nine available bird species were collected and dried - pheasant (Phasianus colchicus), domestic goose (Anser anser domesticus), domestic duck (Anas platyrhynchos domesticus), muscovy duck (Cairina moschata), chicken (Gallus gallus f. domestica) - four different specimens, Japanese quail (Coturnix japonica), pampas rhea (Rhea americana), red ore (Alectoris rufa), speckled guinea fowl (Numida meleagris). The samples were digested by the enzyme trypsin and prepared using ZIP-TIP for the next measurements by MALDI-TOF (Matrix-Assisted Laser Desorption / Ionization - Time of Flight) mass spectrometry. The obtained data were evaluated by Principal Component Analysis (PCA). The used method showed that the most bird species can be distinguished by yolks (with an approximately 95% success rate) and more than half of the species by egg white proteins (success rate 83%) and by the egg white and yolk mixtures (success rate 80%). Two samples of...

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Wastes have a real potential as being players in the energy mix of tomorrow. They can have a high heating value depending on their composition, which makes them good candidates to be converted into liquid fuel via pyrolysis. Among the different types of wastes, automotive residues are expected to rocket due to the increasing number of cars and the tendency to build cars with more and more polymers. Moreover, the existing regulations concerning the recycling of end-of-life vehicles become more and more stringent. Unconventional fuels such as those derived from automotive shredder residues (ASR) have a particular composition which tends to increase the amount of pollutants comparing with conventional fuels. Relying on alternative combustion modes, such as reactivity controlled compression ignition (RCCI), is a solution to cope with these pollutants. In RCCI, two types of fuels are burned simultaneously, namely a light fraction with a low reactivity, and a heavy fraction with a high reactivity. The heavy fraction governs the ignition as it is injected directly in the cylinder close to the end of compression. A variation of its ignition delay could impact the quality of the combustion. Nevertheless, this issue can be tackled by adjusting the injection timing. As long as the low reactivity fuel is concerned, such a solution cannot be adopted as its reactivity depends on the initial parameters (equivalence ratio, inlet temperature, exhaust gas recirculation ratio). However, if the fuel is too reactive, it could create knock that have a dramatic impact on the engine, leading to damages. Thus, being able to predict its features is a key aspect for a safe usage. Predicting methods exist but had never been tested yet with fuels derived from automotive residues. With petroleum products, usual prediction methods stand at three different levels: the chemical composition, the properties, and the reactivity in an appliance. The fuel is studied at these three levels. First, the structure gives a good overview of the fuel auto-ignition. For instance, aromatics tend to have higher ignition delay time (IDT) than paraffins. Second, the octane numbers are good indicators of the fuel IDT and of the resistance toward knock. Precisely, the octane numbers depict the resistance of a fuel towards an end-gas auto-ignition. Last, the IDT was studied in a rapid compression machine and a surrogate fuel was formulated. Surrogate fuels substitute real fuels during simulations because real fuels cannot be modelled by kinetic mechanisms due to their complexity.The existing methods to estimate the composition were updated to predict the n-paraffin, iso-paraffin, olefin, napthene, aromatic and oxygenate(PIONAOx) fractions. A good accuracy was achieved compared with the literature. This new method requires the measurement of the specific gravity, of the distillation cut points, of the CHO atom fractions, of the kinematic viscosity and of the refractive index.Two methods to predict the octane numbers were developed based on Bayesian inference, principal component analysis (PCA) and artificial neural network (ANN). The first is a Bayesian method which modifies the pseudocomponent (PC) method. It introduces a correcting factor which corrects the existing formulation of the PC method to increase its accuracy. A precision of more than 2% is achieved. The second method is based on PCA and ANN. 41 properties are studied among which reduced set of principal variables are selected to predict the octane numbers. 10 properties calculated only with the distillation cut points, the CHO atom fraction and the specific gravity were selected to accurately predict the octane numbers.Measurements of the IDT in a rapid compression machine (RCM) of a fuel produced from ASR were realized. They are the first measurements insuch a machine ever made. This provide experimental data to the literature. Moreover, these experimental data were used to formulate a surrogate fuel. Surrogate fuels can be used to realize simulations under specific conditions. The current thesis investigates fuels derived from ASR. It was showed that this fuel can be burnt in engines as long as their properties are carefully monitored. Among others, the IDT is particularly important. Nevertheless, additional experimental campaigns and simulations in engine are required in order to correctly assess all of the combustion features of such a fuel in an engine. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

With the growing demand for sustainable options, the existing sorting capacities are limiting the potential for fiber-to-fiber recycling. With the help of near-infrared spectroscopy (NIRS), automated sorting of textiles with high accuracy is possible due to the easy access for polymer identification. Despite the effectiveness of NIRS, some limitations of the process still limit its full potential. Possible disruptors may interfere with and disturb the identification of polymer identities and compositions in different ways. In the following thesis, additives, treatments, and other environmental factors that may hinder fiber identification are further acknowledged. The key results of the thesis state that stains and factors due to wear and tear are the most common possible disruptors that could be identified from pre-sorted post-consumer end-of-life textiles. Further on, stains of ketchup, deodorant, and oil affect the polymer recognition by lowering the recognized fiber content. Water-repellent coatings on 100 % polyamide woven fabric were not detected correctly according to the NIR scanner, as the stated polymer composition was >90 %. Even though some investigated factors, e.g., material structures, were correctly identified by the NIR scanner, the internal deviation of the knitted polyester structure indicates that porous and loose structures hold the ability to interfere with the detection of polymers. To what extent the operating software has been developed is highly relevant to the outcome of how accurate textile sorting may be.

Forests in the southern United States with diverse forest ownership entities are facing threats associated with climate change and natural disturbances. This study represented the relationship between climate and species dominance, predicted future species distribution probability under a changing climate, and projected forest dynamics under ownership-based management regimes. Correlative statistics and mechanistic modeling approaches are implemented. Temporal scale includes the recent past 40 years and the future 60 years; spatial scale downscaled from southern United States to the coastal region of the northern Gulf of Mexico. In the southern United States, dominance of four major pine species experienced shifts from 1970 to 2000; quantile regression models built on the relationships among pine dominance and climatic variables can be used to predict future southern pine dominance. Furthermore, multiple climate envelope models (CEMs) were constructed for nineteen native and one invasive tree species (Chinese tallow, Triadica sebifera) to predict species establishment probabilities (SEPs) on the various land types from 2010 to 2070. CEMs achieved both predictive consistency and ecological conformity in estimating SEPs. Chinese tallow was predicted to have the highest invasionability in longleaf/slash pine and oak/gum/cypress forests during the next 60 years. Forest dynamics, in the coastal region, was projected by linking CEMs and forest landscape model (LANDIS) to evaluate ownership-based management regimes under climate change and natural disturbances. The dominance of forest species will diminish due to climate change and natural disturbances at both spatial scales—in the coastal region and non-industrial private forest (NIPF). No management on NIPF land was predicted to substantially increase the ratio of occupancy area between pines and oaks, but moderate and intensive management regimes were not significantly different. Pines are expected to be more resistant than oaks by maintaining stable age structures, which matched the forest inventory records. Overall, this study projected a future of southern forests on climate-species relationship, invasion risks, and forest community dynamics under multiple scenarios in the United States. Such knowledge could assist forest managers and landowners in foreseeing the future and making effective management prescriptions to mitigate potential threats.

Fault detection and diagnosis is a critical component of operations management systems. The goal of FDD is to identify the occurrence and causes of abnormal events. While many approaches are available, data-driven approaches for FDD have proven to be robust and reliable. Exploiting these advantages, the present study applied functional principal component analysis (FPCA) to carry out feature extraction for fault detection in internal combustion engines. Furthermore, a feature subset that explained 95% of the variance of the original vibrational sensor signal was used in a multilayer perceptron to carry out prediction for fault diagnosis. Of the engine states studied in the present work, the ending diagnostic performance shows the proposed approach achieved an overall prediction accuracy of 99.72 %. These results are encouraging because they show the feasibility for applying FPCA for feature extraction which has not been discussed previously within the literature relating to fault detection and diagnosis.

The leaching of groundwater into two polishing ponds, the last step in the wastewater treatment process on Hovgarden waste facility, was examined. The focus of this study was to analyse the PFAS composition profile (fingerprint) to trace the leaching groundwater. PFASs are very persistent man-made substances, used invarious fields and have been linked to several health issues. Polishing pond data and groundwater data for ions and PFAS was collected, compiled with old data and surveyed, mainly by using principle component analysis (PCA). The results indicate that there is a water flow and a mass flow of ions to the ponds, and possibly also a flow of PFAS. The ponds appear to have a different composition, which possible could be the result of a mass flow, however the macro ion distributionis similar. Of the groundwater wells, data suggests that 18G09, P3 IN and P8 were most affected by the landfill. PFOA was the most detected PFAS, and the sampling points with the highest concentration of PFAS was 18G09, P3 IN and the first sampling point (R1) in the wastewater treatment plant. While no apparent correlation between the polishing ponds and groundwater wells were discovered,data suggest that the leaching may come from some of the wells more affected by the landfill.

Reverse Total shoulder arthroplasty (RTSA) is an effective treatment and a surgical alternative approach to conventional total shoulder arthroplasty for patients with severe rotator cuff tears and glenoid erosion. To help optimize RTSA design, it is necessary to gain insight into the geometry of glenoid erosions and consider their unique morphology across the entire bone. One of the most powerful tools to systematically quantify and visualize the variation of bone geometry throughout a population is Statistical Shape Modeling (SSM); this method can assess the variation in the full shape of a bone, rather than of discrete anatomical features, which is very useful in identifying abnormalities, planning surgeries, and improving implant designs. Recently, many scapula SSMs have been presented in the literature; however, each has been created using normal and healthy bones. Therefore, creation of a scapula SSM derived exclusively from patients exhibiting complex glenoid bone erosions is critical and significantly challenging. In addition, several studies have quantified scapular bone properties in patients with complex glenoid erosion. However, because of their discrete nature these analyses cannot be used as the basis for Finite Element Modeling (FEM). Thus, a need exists to systematically quantify the variation of bone properties in a glenoid erosion patient population using a method that captures variation across the entire bone. This can be achieved using Statistical Intensity Modeling (SIM), which can then generate scapula FEMs with realistic bone properties for evaluation of orthopaedic implants. Using an SIM enables researchers to generate models with bone properties that represent a specific, known portion of the population variation, which makes the findings more generalizable. Accordingly, the main purpose of this research is to develop an SSM and SIM to mathematically quantifying the variation of bone geometries in a systematic manner for the complex geometry of scapulae with severe glenoid erosion and to determine the main modes of variation in bone property distribution, which could be used for future FEM studies, respectively. To draw meaningful statistical conclusions from the dataset, we need to compare and relate corresponding parts of the scapula. To achieve this correspondence, 3D triangulated mesh models of 61 scapulae were created from pre-operative CT scans from patients who were treated with RTSA and then a Non-Rigid (NR) registration method was used to morph one Atlas point cloud to the shapes of all other bones. However, the more complex the shape, the more difficult it is to maintain good correspondence. To overcome this challenge, we have adapted and optimized a NR-Iterative Closest Point (ICP) method and applied that on 61 eroded scapulae which results in each bone shape having identical mesh structure (i.e., same number and anatomical location of points). To assess the quality of our proposed algorithm, the resulting correspondence error was evaluated by comparing the positions of ground truth points and the corresponding point locations produced by the algorithm. The average correspondence error of all anatomical landmarks across the two observers was 2.74 mm with inter and intra-observer reliability of ±0.31 and ±0.06 mm. Moreover, the Root-Mean-Square (RMS) and Hausdorff errors of geometric registration between the original and the deformed models were calculated 0.25±0.04 mm and 0.76±0.14 mm, respectively. After registration, Principal Component Analysis (PCA) is applied to the deformed models as a group to describe independent modes of variation in the dataset. The robustness of the SSM is also evaluated using three standard metrics: compactness, generality, and specificity. Regarding compactness, the first 9 principal modes of variations accounted for 95% variability, while the model’s generality error and the calculated specificity over 10,000 instances were found to be 2.6 mm and 2.99 mm, respectively. The SIM results showed that the first mode of variation accounts for overall changes in intensity across the entire bone, while the second mode represented localized changes in the glenoid vault bone quality. The third mode showed changes in intensity at the posterior and inferior glenoid rim associated with posteroinferior glenoid rim erosion which suggests avoiding fixation in this region and preferentially placing screws in the anterosuperior region of the glenoid to improve implant fixation. / Graduate

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