The initiation, bloom, and bust of harmful Cyanobacteria and algae blooms (HAB) in lakes are controlled by top-down and bottom-up ecological controls. Excess phosphorous and nitrogen inputs from anthropogenic sources are primary to blame, but eukaryotic grazers may also promote or curb Cyanobacteria dominance. We tracked shifts in bacterial composition, lake chemistry, and eukaryotic grazing community weekly or bi-weekly through spring and summer and modeled the causes of specific Cyanobacterial species blooms and busts across three lakes in Utah, USA, with differing lake trophic states. Regardless of trophic status, all three lakes experienced blooms of varying composition and duration. Aphanizomenon strain MDT14a was the most dominant species in every bloom on Utah Lake, comprising up to 44.16% of the bacterial community. Utah Lake experienced a total of 18 blooms across all sites ranging in duration from one to six weeks. Phormidiaceae sp. (8.5  6.1%) and Microcystis sp. (9.7  4.7%) were the most abundant species in the Deer Creek bloom. Deer creek experienced one bloom at the beginning of fall. Nodularia sp. (9.7  2.1) dominated Great Salt Lake bloom. The Great Salt Lake experienced four separate blooms during the summer months that lasted one to three weeks. Phosphorous concentrations on Utah Lake varied across site and season. Nitrate concentrations on Deer Creek increased over season with a ten-fold increase in concentration. We characterized Cyanobacteria blooms as either bloom communities (growing populations of Cyanobacteria) or as bust communities (declining populations of Cyanobacteria). Using these designations, we modeled the growth and decline of the Cyanobacteria populations across season with top-down and bottom up-controls. Based on generalized least-squared modeling, eukaryotic grazing does not affect relative Cyanobacteria abundances as much as nutrient limitations. Aphanizomenom strain MDT14a was positively correlated with temperature (P < 0.028) and the concentration of K (P = 0.007) and negatively correlated with increases in conductivity (P = 0.0088). Microcystis was positively correlated with increasing levels of SRP (P < 0.001) and negatively correlated with higher Ca concentrations (P = 0.008) and PP (P = 0.008). Busts of Microcystis were related to decreases in nitrate (P = 0.06) and lower total lake depths (P = 0.03). Phormidiaceae sp. relative abundance was negatively correlated with higher levels of TDN (P = 0.01-0.001) and Mg (P = 0.01) and positively correlated with higher S concentrations (P = 0.007). Our findings suggest that micronutrients and more bioavailable forms of P may potentially allow Cyanobacteria to break dormancy and proliferate HAB communities.

Access to modern energy services derived from renewable sources is a prerequisite, not only for economic growth, rural development and sustainable development, but also for energy security and climate change mitigation. The least developed countries (LDCs) primarily use traditional biomass and have little access to commercial energy sources. They are more vulnerable to problems relating to energy security, air pollution, and the need for hard-cash currency to import fossil fuels. This thesis evaluates sugarcane-molasses bioethanol, a renewable energy source with the potential to be used as a transport fuel in Nepal. Sustainability aspects of molasses-based ethanol have been analyzed. Two important indicators for sustainability, viz. net energy and greenhouse gas (GHG) balances have been used to assess the appropriateness of bioethanol in the life cycle assessment (LCA) framework. This thesis has found that the production of bioethanol is energy-efficient in terms of the fossil fuel inputs required to produce it. Life cycle greenhouse gas (GHG) emissions from production and combustion are also lower than those of gasoline. The impacts of important physical and market parameters, such as sugar cane productivity, the use of fertilizers, energy consumption in different processes, and price have been observed in evaluating the sustainability aspects of bioethanol production. The production potential of bioethanol has been assessed. Concerns relating to the fuel vs. food debate, energy security, and air pollution have also been discussed. The thesis concludes that the major sustainability indicators for molasses ethanol in Nepal are in line with the goals of sustainable development. Thus, Nepal could be a good example for other LDCs when favorable governmental policy, institutional set-ups, and developmental cooperation from donor partners are in place to strengthen the development of renewable energy technologies. / QC 20101029

evaluate disruption risks in the supply chain of petroleum production. This methodology is developed to formalise and facilitate the systematic integration and implementation of various models; such as analytical hierarchy process (AHP) and partial least squares structural equation model (PLS-SEM) and various statistical tests. The methodology is validated with the case of Nigeria’s oil industry. The study revealed the need to provide a responsive approach to managing the influence of geopolitical risk factors affecting supply chain in the petroleum production industry. However, the exploration and production risk, and geopolitical risk were identified as concomitant risk factors that impact performance in Nigeria’s oil industry. The research findings show that behavioural-based mechanisms successfully predict the ability of the petroleum industry to manage supply chain risks. The significant implication for this study is that the current theoretical debate on the supply chain risk management creates the understanding of agency theory as a governing mechanism for supply chain risk in the Nigerian oil industry. The systematic approach results provide an insight and objective information for decisions-making in resolving disruption risk to the petroleum supply chain in Nigeria. Furthermore, this study highlights to stakeholders on how to develop supply chain risk management strategies for mitigating and building resilience in the supply chain in the Nigerian oil industry. The developed systematic method is associated with supply chain risk management and performance measure. The approach facilitates an effective way for the stakeholders to plan according to their risk mitigation strategies. This will consistently help the stakeholders to evaluate supply chain risk and respond to disruptions in supply chain. This capability will allow for efficient management of supply chain and provide the organization with quicker response to customer needs, continuity of supply, lower costs of operations and improve return on investment in the Nigeria oil industry. Therefore, the methodology applied provide a new way for implementing good practice for managing disruption risk in supply chain. Further, the systematic approach provides a simplistic modelling process for disruption risk evaluation for researchers and oil industry professionals. This approach would develop a holistic procedure for monitoring and controlling disruption risk in supply chains practices in Nigeria.

Dans ce mémoire, nous nous intéressons à une façon géométrique de voir la méthode du Lasso en régression linéaire. Le Lasso est une méthode qui, de façon simultanée, estime les coefficients associés aux prédicteurs et sélectionne les prédicteurs importants pour expliquer la variable réponse. Les coefficients sont calculés à l’aide d’algorithmes computationnels. Malgré ses vertus, la méthode du Lasso est forcée de sélectionner au maximum n variables lorsque nous nous situons en grande dimension (p > n). De plus, dans un groupe de variables corrélées, le Lasso sélectionne une variable “au hasard”, sans se soucier du choix de la variable. Pour adresser ces deux problèmes, nous allons nous tourner vers le Lasso Linéaire. Le vecteur réponse est alors vu comme le point focal de l’espace et tous les autres vecteurs de variables explicatives gravitent autour du vecteur réponse. Les angles formés entre le vecteur réponse et les variables explicatives sont supposés fixes et nous serviront de base pour construire la méthode. L’information contenue dans les variables explicatives est projetée sur le vecteur réponse. La théorie sur les modèles linéaires normaux nous permet d’utiliser les moindres carrés ordinaires (MCO) pour les coefficients du Lasso Linéaire. Le Lasso Linéaire (LL) s’effectue en deux étapes. Dans un premier temps, des variables sont écartées du modèle basé sur leur corrélation avec la variable réponse; le nombre de variables écartées (ou ordonnées) lors de cette étape dépend d’un paramètre d’ajustement γ. Par la suite, un critère d’exclusion basé sur la variance de la distribution de la variable réponse est introduit pour retirer (ou ordonner) les variables restantes. Une validation croisée répétée nous guide dans le choix du modèle final. Des simulations sont présentées pour étudier l’algorithme en fonction de différentes valeurs du paramètre d’ajustement γ. Des comparaisons sont effectuées entre le Lasso Linéaire et des méthodes compétitrices en petites dimensions (Ridge, Lasso, SCAD, etc.). Des améliorations dans l’implémentation de la méthode sont suggérées, par exemple l’utilisation de la règle du 1se nous permettant d’obtenir des modèles plus parcimonieux. Une implémentation de l’algorithme LL est fournie dans la fonction R intitulée linlasso, disponible au https://github.com/yanwatts/linlasso. / In this thesis, we are interested in a geometric way of looking at the Lasso method in the context of linear regression. The Lasso is a method that simultaneously estimates the coefficients associated with the predictors and selects the important predictors to explain the response variable. The coefficients are calculated using computational algorithms. Despite its virtues, the Lasso method is forced to select at most n variables when we are in highdimensional contexts (p > n). Moreover, in a group of correlated variables, the Lasso selects a variable “at random”, without caring about the choice of the variable. To address these two problems, we turn to the Linear Lasso. The response vector is then seen as the focal point of the space and all other explanatory variables vectors orbit around the response vector. The angles formed between the response vector and the explanatory variables are assumed to be fixed, and will be used as a basis for constructing the method. The information contained in the explanatory variables is projected onto the response vector. The theory of normal linear models allows us to use ordinary least squares (OLS) for the coefficients of the Linear Lasso. The Linear Lasso (LL) is performed in two steps. First, variables are dropped from the model based on their correlation with the response variable; the number of variables dropped (or ordered) in this step depends on a tuning parameter γ. Then, an exclusion criterion based on the variance of the distribution of the response variable is introduced to remove (or order) the remaining variables. A repeated cross-validation guides us in the choice of the final model. Simulations are presented to study the algorithm for different values of the tuning parameter γ. Comparisons are made between the Linear Lasso and competing methods in small dimensions (Ridge, Lasso, SCAD, etc.). Improvements in the implementation of the method are suggested, for example the use of the 1se rule allowing us to obtain more parsimonious models. An implementation of the LL algorithm is provided in the function R entitled linlasso available at https://github.com/yanwatts/linlasso.

Few studies have explored the relative strength of ecogeographic versus lineage-specific effects on a global scale, particularly for poikilotherms, those organisms whose internal temperature varies with their environment. Here, we compile a global dataset of life history traits in Daphnia, at the species-and population-level, and use those data to parse the relative influences of lineage-specific effects and climate. We also compare the thermal response (plasticity) of life history traits and their dependence on climate, temperature, precipitation, and latitude. We found that the mode of evolution for life history traits varies but that the thermal response of life history traits most often follows a random walk model of evolution. We conclude that life history trait evolution in Daphnia is not strongly species-specific but is ecogeographically distinct, suggesting that life history evolution should be understood at the population level for Daphnia and possibly for other poikilotherms.

The purpose of this research paper was to explore if the adverbial phrase but at least (BAL) is a construction and if any constraints could be identified. To be able to determine a classification, the research focused on finding syntactic and semantic patterns, investigating the definition of constructions as being non-predictable and usage-based. The research was a corpus-based study, analyzing 200 tokens that were extracted from the Corpus of Contemporary American English.The tokens were analyzed by coding them by syntactic features, the semantic use of BAL and the semantics of the whole sentence. The result demonstrated that the BAL-phrase is a construction as a syntactic pattern could be determined, the semantic use of BAL indicates that it is usage-based and it incorporates non-compositional meanings. Furthermore, three constraints could be identified.

Le peuplement des Amériques fut le dernier grand événement migratoire de Homo sapiens et nous méconnaissons toujours les détails à son sujet. Des débats surgissent concernant l’environnement, les populations concernées, ainsi que les cultures impliquées. Malheureusement, des biais scientifiques persistent quant à la chronologie de cet événement et il peut donc être difficile de proposer quelque chose de nouveau. Avec ArcMap 10.7.1, nous présentons de nouveaux modèles de migrations terrestres basés sur les sentiers de moindre effort, retraçant les routes potentielles que les humains ont pu utiliser afin d’arriver en Amérique au cours du Pléistocène; nous surlignons les facteurs environnementaux, génétiques et archéologiques spécifiques qui doivent être considérés pour les modèles futurs, et nous présentons deux trajets de migration qui auraient pu avoir été utilisé pendant le Paléolithique Supérieur, élucidant par conséquent comment les humains sont arrivés pour la première fois dans le continent américain. / The peopling of the Americas was the last great dispersal event of our species, Homo sapiens, and there is still so much we do not know about it. Debates arise concerning the environment, the populations involved, as well as the cultural or physical markers they might have left behind. Unfortunately, the debate concerning the First Peopling of North America is marked by scientific biases and it can thus be difficult to propose something new. Through ArcMap 10.7.1, we present a Least Cost model of terrestrial migrations from Asia to America, we highlight the specific environmental, genetic and archaeological factors that need to be considered in future models, and present two migration paths that could have been used during the Late Pleistocene, thus shedding light onto how humans first arrived in the American continent.

The proliferation of modern wireless networks increases demand for high capacity and throughput in order to provide faster, more robust, efficient and broadband services to end users. Mobile WiMAX and LTE are examples of such networks in which for some cases they have exposed limited connectivity due to harsh environment. Relay stations are preferred to overcome problems of weak or no access for such network devices, that are placed in specific positions to maintain high quality of data transfer at low cost and provide the required connectivity anywhere anytime. These stations should be equipped with an antenna system capable of establishing communication between base station (backhaul link) and end users (access link). This thesis focuses on the design and development of a new antenna system that is suitable for a relay-based wireless network. Planar geometries of microstrip patch antennas are utilized. The antenna system comprises two antenna modules: a new design of a single antenna for access link and a new design of an antenna array for backhaul link realization. Both antenna specifications are compatible with the IEEE802.16j protocol standard. Hence, relay station should be capable of pointing its radiation pattern to the base station antenna, thus to achieve the desired radiation pattern of the relay station, a new beam-forming module is proposed, designed and developed to generate the proper radiation pattern. The beam-forming module incorporating digital phase shifters and attenuator chips is fabricated and tested. The optimization process using the Least Mean Square (LMS) algorithm is considered in this study to assign the proper phase and amplitude that is necessary to each radiation element excitation current, to produce the desired steered radiation pattern. A comprehensive study on the coupling effects for several relative positions between two new backhaul and access link antenna elements is performed. Two new antenna configurations for coupling reduction are tested and the simulated and measured results in terms of antenna radiation performances were compared and commented.

Information seeking and information avoidance are the mechanisms humans natural used for coping with uncertainties and adapting to environmental stressors. Uncertainties are rooted in knowledge gaps. In social sciences, the relationship between knowledge gaps and perceived risk have received little attention. A review of the information science literature suggests that few studies have been devoted to the investigation of the role of this relationship in motivating information-seeking behavior. As an effort to address the lack of theory building in the field of information science, this study attempts to construct a model of information seeking under risk (MISR) by examining the relationships among perceived risk, knowledge gap, fear arousal, risk propensity, personal relevance, and deprivation and interest curiosity as antecedents to motivation to seek information. An experimental approach and a scenario-based survey method are employed to design the study. Partial least square structural equation modeling (PLS-SEM) analysis was conducted to test the relationships in the proposed model. Perceived risk was found to be a highly significant predictor of information seeking in moderately high-risk situations. Similarly, personal relevant has a significant negative effect on perceived risk and its interaction with knowledge gap motivates information seeking.

Data gathering is a constant in human history with ever increasing amounts in quantity and dimensionality. To get a feel for the data, make it interpretable, or find underlying laws it is necessary to fit a function to the finite and possibly noisy data. In this thesis we focus on a method achieving this, namely least squares approximation. Its discovery dates back to around 1800 and it has since then proven to be an indispensable tool which is efficient and has the capability to achieve optimal error when used right. Crucial for the least squares method are the ansatz functions and the sampling points. To discuss them, we gather tools from probability theory, frame subsampling, and $L_2$-Marcinkiewicz-Zygmund inequalities. With that we give results in the worst-case or minmax setting, when a set of points is sought for approximating a class of functions, which we model as a generic reproducing kernel Hilbert space. Further, we give error bounds in the statistical learning setting for approximating individual functions from possibly noisy samples. Here, we include the covariate-shift setting as a subfield of transfer learning. In a natural way a parameter choice question arises for balancing over- and underfitting effect. We tackle this by using the cross-validation score, for which we show a fast way of computing as well as prove the goodness thereof.:1 Introduction 2 Least squares approximation 3 Reproducing kernel Hilbert spaces (RKHS) 4 Concentration inequalities 5 Subsampling of finite frames 6 L2 -Marcinkiewicz-Zygmund (MZ) inequalities 7 Least squares in the worst-case setting 8 Least squares in statistical learning 9 Cross-validation 10 Outlook