Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire a deeper insight into the environmental factors affecting their productivity. In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms. We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors. Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.

Biotic interactions are known to shape natural community assemblages and biodiversity. Positive interactions such as facilitation have recently received attention in ecological food webs. Mechanistic models have improved our understanding of these complex food web interactions. Here, focus is given to a three-species food web system with a beach dune natural community in mind. In the last decade, there has been a series of studies investigating intraguild predation between two major loggerhead sea turtle nest predators, North American raccoons and Atlantic ghost crabs. Studies have also highlighted that ghost crab predation assists raccoons in finding nests (i.e., facilitated predation). However, the combined effects of these two intraguild interactions and their consequences on nests have not been examined explicitly. The aims of this study were to (i) develop a three-species, ordinary differential equation model (ii) implement a sensitivity analysis to understand the influence of facilitation and other factors in driving species richness and abundance and (iii) characterize the dynamic interactions between intraguild predators and their effects on a shared resource. Interactions between ghost crabs and sea turtle eggs and facilitation can yield a wide variety of species abundance responses and were influential factors in the model. I found that high secondary sea turtle egg depredation and low facilitated predation by raccoons led to three species co-existence regions in the model. Controlling for nest predators at higher abundance levels showed that ghost crabs had a larger negative effect on sea turtle egg abundance responses when compared to raccoons. This suggests that interactions between sea turtle eggs and ghost crabs appear to be important and potential sea turtle nest management implications are discussed such as the use of ghost crab exclusion devices.

The current study is an experimental and analytical investigation of JIB within the nucleate and transition boiling regimes. This study focuses on studying JIB within the stagnation zone of a free water jet. An experimental setup has been designed and built at the Thermal Processing Laboratory (TPL) with the capability of carrying out boiling experiments at heat fluxes up to 12 MW/m2. The JIB curves have been obtained under steady-state conditions for a wide range of jet conditions, higher than those considered during previous JIB studies. The effect of jet velocity, up to 3.8 m/s, and degree of subcooling, up to 49 °C, on the JIB curve has been studied. The results showed that both jet velocity and degree of subcooling have a weak effect on the nucleate boiling regime and significantly affect the transition boiling regime. Bubble dynamics under the impinging jet within the nucleate boiling regime and the stability of the vapor layer within the transition boiling regime have been investigated. An analytical mechanistic model, based on force balance and thermal balance equations, has been developed to predict the bubble growth rate and the BDD. The developed model was validated using current experimental data. The model gave a relative deviation of 17.8 %. Results of the mechanistic model within the stagnation zone showed that, amongst the three heat transfer mechanisms that affect bubble growth (i.e., the microlayer evaporation, the heat from the superheated layer, the convection heat loss to subcooled liquid), the microlayer evaporation is the most significant contributor to the rate of bubble growth. The current work conducted within the transition boiling regime was focused on the determination of the total wall heat flux within the stagnation zone, both experimentally and analytically. Steady-state experiments have been carried out during which the vapor layer stability was examined. The vapor layer breakup frequency was measured using a fiber-optic probe. Experiments were conducted at a jet velocity of 1 m/s and degrees of subcooling between 11 and 49 ºC. / Thesis / Doctor of Philosophy (PhD)

Cellulose from energy crops or agriculture residues can be utilized as a sustainable energy resource to produce biofuels such as ethanol. The process of converting cellulose into solvents and biofuels requires the saccharification of cellulose into soluble, fermentable sugars. However, challenges to cellulosic biofuel production include increasing the activity of cellulose-degrading enzymes (cellulases) and increasing solvent (ethanol) yield while minimizing the co-production of organic acids. This work applies novel surface analysis techniques and fermentation reactor perturbations to quantify, manipulate, and model enzymatic and metabolic processes critical to the efficient production of cellulosic biofuels. Surface analysis techniques utilizing cellulose thin film as the model substrate are developed to quantify the kinetics of cellulose degradation by cellulase as well as the interactions with cellulase at the interfacial level. Quartz Crystal Microbalance with Dissipation (QCM-D) is utilized to monitor the change in mass of model cellulose thin films cast. The time-dependent frequency response of the QCM simultaneously measures both enzyme adsorption and hydrolysis of the cellulose thin film by fungal cellulases, in which a significant reduction in the extent of hydrolysis can be observed with increasing cellobiose concentrations. A mechanistic enzyme reaction scheme is successfully applied to the QCM frequency response for the first time, describing adsorption/desorption and hydrolysis events of the enzyme, inhibitor, and enzyme/inhibitor complexes. The effect of fungal cellulase concentration on hydrolysis is tested using the QCM frequency response of cellulose thin films. Atomic Force Microscopy (AFM) is also applied for the first time to the whole cell cellulases of the bacterium C. thermocellum, where the effect of temperature on hydrolysis activity is quantified. Fermentation of soluble sugars to desirable products requires the optimization of product yield and selectivity of the cellulolytic bacterium, Clostridium thermocellum. Metabolic tools to map the phenotype toward desirable solvent production are developed through environmental perturbation. A significant change in product selectivity toward ethanol production is achieved with exogenous hydrogen and the addition of hydrogenase inhibitors (e.g. methyl viologen). These results demonstrate compensatory product formation in which the shift in metabolic activity can be achieved through environmental perturbation without permanent change in the organism’s genome.

In this dissertation, I examine the role that intentional descriptions play in our scientific study of the mind. Behavioural scientists often use intentional language in their characterization of cognitive systems, making reference to “beliefs”, “representations”, or “states of information”. What is the scientific value gained from employing such intentional terminology? I begin the dissertation by contrasting intentional descriptions with mechanistic descriptions, as these are the descriptions most commonly used to provide explanations in the behavioural sciences. I then examine the way that intentional descriptions are employed in various scientific contexts. I conclude that while mechanistic descriptions characterize the underlying structure of systems, intentional descriptions allow us to generate predictions of systems while remaining agnostic as to their mechanistic underpinnings. Having established this, I then argue that intentional descriptions share much in common with statistical models in the way they characterize systems. Given these similarities, I theorize that intentional descriptions are employed within scientific practice as a particular type of phenomenological model. Phenomenological models are used to study, characterize, and predict the phenomena produced by mechanistic systems without describing their underlying structure. I demonstrate why such models are integral to our scientific discovery, and understanding, of the mechanisms that make up the brain. With my account on the table, I then look back at previous accounts of intentional language that philosophers have offered in the past. I highlight insights that each brought to our understanding of intentional language, and point out where each ultimately goes astray. I conclude the dissertation by examining the ontological implications of my theory. I demonstrate that my account is compatible with versions of both realism, and anti-realism, regarding the existence of intentional states.

In this dissertation, I examine the role that intentional descriptions play in our scientific study of the mind. Behavioural scientists often use intentional language in their characterization of cognitive systems, making reference to “beliefs”, “representations”, or “states of information”. What is the scientific value gained from employing such intentional terminology? I begin the dissertation by contrasting intentional descriptions with mechanistic descriptions, as these are the descriptions most commonly used to provide explanations in the behavioural sciences. I then examine the way that intentional descriptions are employed in various scientific contexts. I conclude that while mechanistic descriptions characterize the underlying structure of systems, intentional descriptions allow us to generate predictions of systems while remaining agnostic as to their mechanistic underpinnings. Having established this, I then argue that intentional descriptions share much in common with statistical models in the way they characterize systems. Given these similarities, I theorize that intentional descriptions are employed within scientific practice as a particular type of phenomenological model. Phenomenological models are used to study, characterize, and predict the phenomena produced by mechanistic systems without describing their underlying structure. I demonstrate why such models are integral to our scientific discovery, and understanding, of the mechanisms that make up the brain. With my account on the table, I then look back at previous accounts of intentional language that philosophers have offered in the past. I highlight insights that each brought to our understanding of intentional language, and point out where each ultimately goes astray. I conclude the dissertation by examining the ontological implications of my theory. I demonstrate that my account is compatible with versions of both realism, and anti-realism, regarding the existence of intentional states.

Lors des rejets accidentels ou chroniques des installations nucléaires vers l'environnement, il est important de prédire le devenir des radionucléides dans l'environnement, en particulier à l'échelle de la prairie qui constitue un des maillons de la chaîne alimentaire. L'IRSN et EDF développent une plateforme de simulation pour le transfert de radionucléides dans l'environnement appelé SYMBIOSE, afin d'estimer la dose reçue par les humains. Le tritium est un exemple d'un radionucléide rejeté dans l'environnement par les installations nucléaires, dont le comportement est particulièrement difficile à prédire en raison de sa grande mobilité et de son affinité biologique selon la forme chimique considérée. Le modèle TOCATTA-khi, mis en œuvre dans la plateforme SYMBIOSE, s’intéresse entre autres aux transferts de tritium au sein d’un écosystème prairial. L'objectif principal de la thèse est d'améliorer et d'évaluer le modèle existant de TOCATTA-khi à partir d'expériences réalisées in situ à la plateforme technique IRSN La Hague et en laboratoire, en étudiant et quantifiant les transferts de tritium au sein d'un écosystème prairial, ainsi que les incertitudes rémanentes associées. / In the context of accidental or chronic releases from nuclear installations to the environment, it is important to predict the future of radionuclides, particularly at the grassland scale, the first link in the human food chain. The IRSN and EDF are developing a simulation platform for the transfer of radionuclides in the environment, called SYMBIOSE, in order to estimate the dose received by humans. Tritium is an example of a radionuclide released into the environment by nuclear installations, whose behavior is particularly critical to predict because of its high mobility and biological affinity depending on the chemical form considered. The TOCATTA-khi model, implemented in the SYMBIOSE platform, is dedicated, among other things, to the transfer of tritium within a grassland ecosystem. The main objective of this thesis is to improve and evaluate the existing TOCATTA-khi model from experiments carried out in situ at the technical platform IRSN La Hague and in laboratory, by studying and quantifying the transfers of tritium within a grassland ecosystem, as well as the associated residual uncertainties.

No description available.

The compacted bentonites are used as buffer and backfill materials for engineering barriers for high-level nuclear waste repositories located underground. For this purpose, it is very important to evaluate the swelling characteristics of this clay. The swelling capacity is one of the most important properties of the bentonite clay. The swelling behaviour is due two mechanisms, the crystalline swelling and the osmotic swelling. These mechanisms produce an increase in the distance between the layers of montmorillonite which is one component of bentonite. The result of the swelling capacity is the swelling pressure. It has been studied in several investigations. The results of experimental tests have been collected and compared in this thesis, considering only the Na-dominant bentonite and sand and distilled water as test solution. The experimental tests show that there is only an unique relation between different bentonites in the Na-dominant bentonite and sand mixture swollen depending on its final dry density. Also, the relation between the swelling pressure and the clay void ratio shows the mechanism of the swelling pressure. On the other hand, a mechanistic model is used to predict the swelling pressure of fully saturated bentonite and sand mixture in distilled water. Firstly, it has been compared with the results of experimental tests and it should be pointed out that the model gives good predictions. In addition, the model has been used to make sensitivity analysis with different parameters of bentonite. The most important conclusions in this section show that the swelling pressure mainly depends on the distance among particles. Also, the sensitivity analyses indicate which parameters should be fitted more carefully for future studies to validate this model with different bentonites.

No description available.