Many studies have previously been done on moisture-induced strains in wood. An in- finitesimal/engineering strain model has been used for most of these studies, which is often an accurate approximation for small rotations. However, if large deformations oc- cur, then fictive strains are obtained resulting from the simplified engineering strain.  This work aims to develop a finite element formulation for problems of moisture- induced strains in orthotropic materials based on the total Lagrangian approach, where large displacements and rotations are considered. This model is then used to examine static drying deformations and their effect on dynamic vibrations. A dynamic vibration test was also done to estimate the modulus of elasticity in the fibre direction. The pur- pose is to increase the understanding of moisture-induced strains in wood and also to emphasize the advantages of using a large deformation model.  To facilitate the understanding of large deformation theory, the implementation is first done for a 2D isotropic beam where static and dynamic simulations are made. Re- sults will be compared with a standard model based on engineering strains. For the static part, two types of wooden species are studied, radiata pine and Norway spruce, and com- pared with a previous research study [32] where engineering strain theory is used. The dynamical considerations are divided into a theoretical and an experimental part. The theoretical part analyzes the vibration of radiata pine and Norway spruce samples from a study by Cown and Ormarsson 2005 [32]. In the experimental part, three Norway spruce boards were analyzed.  The results from the numerical implementation showed, among other things, that by taking moisture-induced strains into account two additional properties, the matrix Gm and the vector Emf appear in the finite element formulation. It was concluded that by using a large deformation model the accuracy will increase without causing any extra computational costs.  The transient numerical mass flow analysis showed reasonable results although the sorption exchange rate has to be slightly higher than indicated by comparable measure- ments. For the dynamic part, the performed experiment showed a difference in response between the three Norway spruce species. It was shown that the frequency increases with distance from the pith and also with lower moisture content. The difference in vibration response between Norway spruce and radiata pine was analyzed based on boards from a study by Cown and Ormarsson 2005 [32]. The response for Norway spruce tends to show a higher frequency compared to radiata pine for the test performed in this investigation. This is mainly due to a higher modulus of elasticity and lower density for Norway spruce compared to radiata pine.

Pathogens in low-moisture foods are an emerging food safety concern due to increased survival and thermotolerance in matrices with low water activity. However, limited data is publicly available for the thermotolerance of Listeria monocytogenes, Salmonella spp., and Enterococcus faecium NRRL B-2354 (a Salmonella surrogate). The aims of this study were to identify differences in thermal inactivation rates between these organisms in three different low-moisture foods. Three model low-moisture foods (peanut butter, powder infant formula, and wheat flour) were inoculated with either E. faecium, a Salmonella spp. cocktail, or a L. monocytogenes cocktail using a dry inoculation method for a total of 9 treatments. Samples were heat treated in a hot water bath at predetermined temperatures, and bacterial survival was detected via direct plating on tryptic soy agar with 0.6% yeast extract. In peanut butter and most of the powder infant formula treatments, Salmonella spp. had significantly higher D-values than L. monocytogenes using comparable temperatures (p < 0.05). However, D-values between Salmonella spp. and L. monocytogenes were comparable in wheat flour and one of the treatment temperatures in powder infant formula (p > 0.05). For all but one of the treatments at the same temperature, E. faecium had significantly higher D-values than L. monocytogenes and Salmonella spp. in each food matrix (p < 0.05). The observed matrix effect on thermotolerance for each of the bacteria was reported in descending order as powder infant formula > peanut butter > wheat flour in the majority of the comparable D-values. While Salmonella continues to be the pathogen of concern in low-moisture foods due to survival and outbreaks, these results indicate L. monocytogenes can exhibit similar thermotolerances in relevant model low-moisture foods matrices.

Vegetative, non-sporeforming foodborne pathogens show notable survival and uncanny thermotolerance in low water activity (aw) foods. Controlled studies on Listeria monocytogenes, Salmonella spp., and Enterococcus faecium NRRL B-2354 (a Salmonella surrogate) in a variety of food matrices support thermal process validation studies required to achieve global food safety objectives. In this study, we determined and compared thermal inactivation rates using independent six-strain cocktails of pathogens in three plant-based butters. Direct determinations of decimal reduction times (D-values) for L. monocytogenes, Salmonella, and E. faecium, in corresponding butters were inoculated using peanut oil, almond oil, or sunflower oil. Thermal Death Time (TDT) studies for the organisms were conducted in triplicate. Uniform bagged plant- based butter samples of Salmonella spp. or L. monocytogenes, or E. faecium alone were sandwiched in copper plates immobilized with recessed magnets. Samples underwent rapid heat treatments via water immersion under isothermal conditions ranging from 70°C to 85°C. Bacterial destruction in peanut butter (46% fat, 0.20 aw @ 25°C), almond butter, (50% fat, 0.32 aw @ 25°C), or sunflower butter (56% fat, 0.15 aw @ 25°C) was determined by direct plating. The TDT studies showed Salmonella spp. had consistently higher D-values than L. monocytogenes in all treatments, but pair-wise comparisons found no statistical difference when assessing the thermotolerance of the two pathogens in the individual plant-based butters tested (p > 0.005). These data support Salmonella as the primary pathogen of concern in low water activity foods and show the heat resistance of L. monocytogenes can approximate destruction kinetics observed for Salmonella spp. in low aw matrices. E. faecium exhibited the highest thermotolerance. This further supports the utility of this surrogate for Salmonella spp. and L. monocytogenes in high fat, low-moisture foods similar to the plant-based butters tested. Thermotolerance differences between a dry talc vs. peanut oil-based inoculation procedures in peanut butter were also evaluated. Surprisingly, the oil-based inoculations resulted in lower D- values (p > 0.01) for Salmonella spp. and the surrogate when compared to the dry inoculum.

Floods associated with extreme precipitation are one of the most serious natural hazards, which produce substantial human and socio-economic losses in central Europe. One way to reduce the impact of flooding is by increasing preparedness with better flood forecasts and warnings, which is not possible without a proper understanding of physical processes leading to a flood hazard. However, frequent research on floods in relation to causal precipitation and synoptic conditions is usually carried out regionally, although some events often affect areas of a size of entire countries or even larger. The thesis was focused exactly on these large-scale precipitation and flood events that occurred in the second half of the 20th century and then until 2013, for which the size of the affected area is as crucial in the extremity assessment as the magnitude of flood discharges or precipitation totals. The extremity indices used for the assessment of extreme precipitation and flood events connected both aspects. The larger area of interest defined within central Europe allowed examining the spatial structure of events, the differences between them, and their relation to conditions in the atmosphere. To connect the extremes of precipitation with extremes in atmospheric conditions, the causal circulation was...

Betong och trä är två material som används till prefabricerade stomsystem. Dessa material har förutom utseende olika tekniska egenskaper för fukt, brand och ljud. Syftet med examensarbetet är att göra en jämförelse av de tekniska egenskaperna för prefabricerade betongstommar (sandwich-element) och prefabricerade trästommar (massivträ) samt att avgöra vad som är mest lämpligt för flervåningshus (två–fem våningar). Resultatet visar att båda stommarna går att använda för flervåningshus, men för att få tillräcklig brandsäkerhet behöver trästommen en större dimension. Trä påverkas mer och lättare av fukt än betong. Det kan även behövas en dubbelkonstruktion för väggar och bjälklag i trä för att ta bort de ljudbryggorna som skapas. Detta gör att dimensioneringen av bjälklag och väggar av trä blir större än för betong.  Träkonstruktioner har också högre krav på knutpunkter mellan byggdelarna för att minimera flanktransmissionerna. För betongstommar är det de tillhörande byggnadsdelarna som fönster och uteluftsintag som i större grad påverkar ljudisoleringsförmågan. Slutsatsen är att prefabricerade trä- och betongstommar har olika tekniska egenskaper sett till brand, fukt och ljud. Trä är mer känsligt för brand, fukt och ljud. Det ställs därför högre krav på utformningen av konstruktionen och hanteringen av trästommar för att uppnå samma egenskaper som en betongstomme. Trästommen får oftast en större dimensionering för att klara kraven som ställs på de tekniska egenskaperna och det är framförallt ljudkravet som blir avgörande för dimensioneringen.

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Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spatial scales. In this dissertation, solutions were proposed in three problem areas that can be important in the decision making process related to water management in irrigated systems. A data assimilation technique was used to build a learning machine model that generated soil moisture estimates commensurate with the scale of the data. The research was taken further by developing a multivariate machine learning algorithm to predict root zone soil moisture both in space and time. Further, a model was developed for supervised classification of multi-spectral reflectance data using a multi-class machine learning algorithm. The procedure was designed for classifying crops but the model is data dependent and can be used with other datasets and hence can be applied to other landcover classification problems. The dissertation compared the performance of relevance vector and the support vector machines in estimating soil moisture. A multivariate relevance vector machine algorithm was tested in the spatio-temporal prediction of soil moisture, and the multi-class relevance vector machine model was used for classifying different crop types. It was concluded that the classification scheme may uncover important data patterns contributing greatly to knowledge bases, and to scientific and medical research. The results for the soil moisture models would give a rough idea to farmers/irrigators about the moisture status of their fields and also about the productivity. The models are part of the framework which is devised in an attempt to provide tools to support irrigation system operational decisions. This information could help in the overall improvement of agricultural water management practices for large irrigation systems. Conclusions were reached based on the performance of these machines in estimating soil moisture using remotely sensed data, forecasting spatial and temporal variation of soil moisture and data classification. These solutions provide a new perspective to problem–solving techniques by introducing new methods that have never been previously attempted.

The United States produces about 1.8 million tons of sugar annually. Approximately 75 per cent of this production is derived from sugar beets. The importance of the sugar beet crop in national and world economy is justification for research effort as a means to more economical production. It is desirable that sugar beet processing be carried out in the most efficient manner. More effective utilization of the sugar beet and its by-products will add stability to the sugar beet industry. For the past 170 years, since Achard found that sugar could be used for human consumption and that pulp might be fed to cattle, sugar processors have made limited use of the non-sugar constituents of the sugar beet. These materials have been disposed of almost exclusively as livestock ration supplements. The non-sugar constituents have been largely responsible for failure to extract all of the sugar from the beet (13). As a result they have been viewed with suspicion by most sugar beet processors. However, recent development suggest that the utilization of sugar beet by-products will constitute a more important phase of the sugar beet industry in the future. At this critical period in the sugar beet industry, it is difficult to overemphasize the need for a better understanding of the chemical constituents of the sugar beet and the effects of various physiological factors upon them. One of the non-sugar constituents of the sugar beet which has recently received attention is glutamic acid. This has been brought about primarily by the discovery that the salt, monosodium glutamate, has an enhancing effect upon the flavor and palatability of many foods. Using the sugar beet as almost the exclusive source of glutamic acid, a new industry (utilizing over 100 tons of beet molasses daily) has developed to manufacture and market this food seasoner (28). Preliminary investigations at the Utah Experiment Station (14) showed that of all the chemical constituents determined, glutamic acid was the most variable. This agreed with earlier work in this field (16, 42). Being highly variable this constituent is a chief contributor to difficulties in sugar processing. The purpose of this study was to determine the effect of some of the major agronomic factors, such as moisture, fertility, variety, and sampling date, upon the glutamic acid content of the sugar beet.

Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.

L'humidité du sol est une variable climatique essentielle dont le suivi à l'échelle globale est possible grâce à des instruments micro-ondes à bord des satellites. La précision de ces estimations est validée par comparaison directe aux mesures au sol. Tandis que les estimations satellitaires ont des résolutions allant de 30 à 100 km, les capteurs in situ sont généralement représentatifs d'une zone de quelques centimètres (résolution ponctuelle). Cette différence entre l'échelle spatiale des estimations satellitaires et in situ impacte le processus de validation et les statistiques obtenues à un niveau qui n'est pas connu actuellement. Cette thèse vise à améliorer la connaissance de l'impact du changement d'échelle spatiale, ainsi qu'à fournir des méthodes d'évaluation de celle-ci applicables à toute zone de validation. Pour ce faire, la relation entre les échelles spatiales et temporelles a été étudiée. Des séries modélisées et mesurées sur des régions différentes du globe ont été décomposées en échelles de temps allant de 0,5 et 128 jours, en utilisant des transformées en ondelettes. La représentativité spatiale des mesures à résolution ponctuelle a ensuite été évaluée, par échelle de temps, avec 4 approches différentes : l'analyse de la stabilité temporelle, la triple colocation, le pourcentage de zones corrélées (CArea) et une nouvelle approche utilisant des corrélations basées sur des ondelettes (WCor). De plus, l'incertitude d'échantillonnage a été évaluée séparément avec des approches bootstrap et des simulations de Monte Carlo de séries à résolution ponctuelle. À l'issue de ces expériences, il y a été constaté que la moyenne des valeurs de représentativité spatiale obtenues tend à augmenter avec l'échelle de temps, mais aussi leur dispersion. Cela implique que certaines stations ont de vastes zones de représentativité à des échelles saisonnières, tandis que d'autres ne l'ont pas. Aux échelles sous-hebdomadaires, toutes les stations présentaient de très petites zones de représentativité. Enfin, l'impact de l'incertitude d'échantillonnage s'est avéré assez important dans les métriques de validation satellitaire. / Soil moisture is an essential climate variable that is globally monitored with the help of satellite borne microwave instruments. The accuracy of satellite soil moisture estimations is assessed by direct comparison to in situ measurements. While satellite estimates have a resolution ranging between 30 and 100 km, in situ sensors typically measure over an area of a few centimetres (point resolution). This spatial scale mismatch between satellite and in situ soil moisture estimates impairs the validation process and the respective summary statistics to an extent that is not currently known. This thesis aims at improving the knowledge of the spatial scale mismatch, as well as providing methods for its assessment applicable to any validation area. To this end, the connection between the SM spatial and time scales was investigated. Modelled and measured soil moisture series at different regions of the globe were decomposed into time scales ranging from 0.5 to 128 days, using wavelet transforms. The spatial representativeness of the point measurements was then assessed, on a per time scale basis, with 4 different approaches: temporal stability analysis, triple collocation, the percentage of correlated areas (CArea) and a new approach that uses wavelet- based correlations (WCor). Moreover, one of the components of the mismatch, the sampling uncertainty, has been assessed separately with bootstrap and Monte Carlo simulations of point-support series. It was found that the average of the spatial representativeness values tends to increase with the time scales but so does their dispersion. This implies that some stations had large representativeness areas at seasonal scales, while others do not. At sub-weekly scales, all stations exhibited very small representativeness areas. Finally, the sampling uncertainty has been observed to have a considerable impact on satellite validation statistics.