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Abstract: Model-based control and optimization is the predominant paradigm in process systems engineering. The performance of model-based methods, however, rely heavily on the accuracy of the process model, which declines over the operation cycle due to various causes, such as catalyst deactivation, equipment aging, feedstock variability, and others. This work aims to tackle this challenge by considering two alternative approaches. The first approach replaces existing control and optimization methods with model-free reinforcement learning (RL). We apply a state-of-the-art reinforcement learning algorithm to a network of reactions, evaluate the performance of the RL controller in terms of setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, and optimize the reward function to achieve the desired control and optimization performance. The second approach presents a novel framework for integrating Economic Model Predictive Control (EMPC) and RL for online model parameters estimation. In this framework, EMPC optimally operates the closed-loop system while maintaining closed-loop stability and recursive feasibility. At the same time, the RL agent continuously compares the measured state of the process with the model’s predictions, and modifies the model parameters accordingly to optimize the process. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance.

<p>Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures to target apply herbicide on per-weed basis within a field. However, a major challenge to developing a weed detection system is the requirement for properly annotated training data to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of weeds by using UAS-acquired imagery from corn and soybean research plots located in North-central Indiana. A total of 27,828 RGB; 108,398 multispectral; and 23,628 thermal images, were acquired using FLIR Duo Pro R sensor that was attached to a DJI Matrice 600 Pro UAS. An annotated database of 306 RGB images, organized into monocot and dicot weed classes, was used for network training. Two Deep Learning networks namely, DetectNet and You Only Look Once version 3 (YOLO ver3) were subjected to five training stages using four annotated image sets. The precision for weed detection ranged between 3.63-65.37% for monocot and 4.22-45.13% for dicot weed detection. This research has demonstrated a need for creating a large annotated weed database for improving precision of deep learning algorithms through better training of the network.</p>

Artifical intelligence is a part of the progress of the digitalization in the society. Language models such as ChatGPT have the ability to answer questions posed by the user as well as to produce texts that resemble human writing. However, the use of language models is not without risk. They can be used as a tool to encourage cheating on school assignments or to contribute to disinformation. A consequence of the development of AI is that knowledge is obtained in different ways compared to the past, which has to come to transform the traditional teaching methods in schools. The purpose of the study is to investigate how AI affects the tuition in social studies and also what experience social studies teachers have of AI in connection with teaching. The study focuses in upper secondary school in Sweden. The empirical material has been collected through semi-structured interviews. The results of the study show that the teachers see both opportunities and challenges with AI in teaching. AI can be used as a resource by both teachers and students according to the teachers participating in this study. However the result shows that AI also can have an negative impact on the students learning process. Regardless, teachers have been forced to make changes in their teaching.

Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.

Aus der Einführung: "Stehen wir am Rande einer bio-nanotechnologischen getriebenen Revolution, die unsere Art zu leben, zu arbeiten und miteinander umzugehen grundlegend verändern wird? Welchem gesellschaftspolitischen, wirtschaftlichen und technologischen Wandel haben wir uns zu stellen? Langfristige Entwicklungszyklen (Kondratieff, Schumpeter) führen zur nachhaltigen Weiterentwicklung der Zivilisation. Mittelfristige Entwicklungen wie die Trends Globalisierung, Urbanisierung, Digitalisierung (Miniaturisierung) und Humanisierung (Individualisierung), die immer stärker unser Umfeld und Handeln beeinflussen führen zu ganzheitlichen, weltumspannenden Grundtendenzen der gesellschaftlichen Weiterentwicklung. Die technologischen "Enabler" Computing, Biotechnology, Artifical Intelligence, Robotik, Nanotechnology, Additive Manufacturing und Design Thinking wirken beschleunigend auf die gesellschaftlichen Entwicklungen ein. Die technologischen Möglichkeiten beschleunigen sowohl gesellschaftspolitische Zyklen und zivilisatorische Anpassungen. Durch rasanten technologischen, wissenschaftlichen Fortschritt, zunehmende Globalisierungswirkungen, beschleunigte Urbanisierung und aber auch politischer Interferenzen sind die Veränderungsparameter eines dynamischen Geschäftsumfelds immer schnellere Transformationen ausgesetzt. Alle diese Richtungen zeigen das unsere gesellschaftliche Entwicklung inzwischen stark durch die Technik getrieben ist. Ob dies auch heißt, dass wir den Punkt der Singularität (Kurzweil) absehbar erreichen ist dennoch noch offen. ..."

The evolution of disease resistance in plants occurs within a framework of interacting phenotypes, balancing natural selection for life-history traits along a continuum of fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant populations connected by gene flow are physiologically limited to evolving along a single axis of the spectrum of the growth-defense trade-off, and strong local selection can purge phenotypic variance from a population or species, making it difficult to detect variation linked to the trade-off. Hybridization between two species that have evolved different growth-defense trade-off optima can reveal trade-offs hidden in either species by introducing phenotypic and genetic variance. Here, I investigated the phenotypic and genetic basis for variation of disease resistance in a set of naturally formed hybrid poplars. The focal species of this dissertation were the balsam poplar (Populus balsamifera), black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from natural populations and clonally replicated in a common garden. Ecophysiology and stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae) were collected. To overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata. To identify stomata, a deep con- volutional neural network was trained on over 4,000 cuticle images of over 700 plant species. The neural network had an accuracy of 94.2% when applied to new cuticle images and phenotyped hundreds of micrographs in a matter of minutes. To understand how disease severity, stomata, and ecophysiology traits changed as a result of hybridization, statistical models were fit that included the expected proportion of the genome from either parental species in a hybrid. These models in- dicated that the ratio of stomata on the upper surface of the leaf to the total number of stomata was strongly linked to disease, was highly heritable, and wass sensitive to hybridization. I further investigated the genomic basis of stomata-linked disease variation by performing an association genetic analysis that explicitly incorporated admixture. Positive selection in genes involved in guard cell regulation, immune sys- tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis were identified. Together, my dissertation incorporated advances in image-based phenotyping with evolutionary theory, directed at understanding how disease frequency changes when hybridization alters the genomes of a population.

The model of this thesis simulates a simple artificial eco-system in which evolving and learning agents try to survive by consuming balls of energy and surviving attacks by other agents. The study finds that the model indeed manages to evolve surviving, and in some cases very aggressive, agents. The thesis presents similar conclusions to that of the study of Polyworld by Yaeger [16]; that an evolving population only facilitates a need for complexity set by the world it evolves in and stagnates when the population has reached this level of complexity. If the populations are to evolve further, the world it lives in must first demand a higher level of complexity. Various problems with simulating artificial life are also discussed along with the more specific obstacles of simulating artificial life in Breve and NEST integrated. The physical world of the model is built in the Breve simulation environment and the neural networks are simulated in NEST through integrate-and-fire neurons and spike-timing dependent plasticity synapses.

Aus der Einführung: "Stehen wir am Rande einer bio-nanotechnologischen getriebenen Revolution, die unsere Art zu leben, zu arbeiten und miteinander umzugehen grundlegend verändern wird? Welchem gesellschaftspolitischen, wirtschaftlichen und technologischen Wandel haben wir uns zu stellen? Langfristige Entwicklungszyklen (Kondratieff, Schumpeter) führen zur nachhaltigen Weiterentwicklung der Zivilisation. Mittelfristige Entwicklungen wie die Trends Globalisierung, Urbanisierung, Digitalisierung (Miniaturisierung) und Humanisierung (Individualisierung), die immer stärker unser Umfeld und Handeln beeinflussen führen zu ganzheitlichen, weltumspannenden Grundtendenzen der gesellschaftlichen Weiterentwicklung. Die technologischen "Enabler" Computing, Biotechnology, Artifical Intelligence, Robotik, Nanotechnology, Additive Manufacturing und Design Thinking wirken beschleunigend auf die gesellschaftlichen Entwicklungen ein. Die technologischen Möglichkeiten beschleunigen sowohl gesellschaftspolitische Zyklen und zivilisatorische Anpassungen. Durch rasanten technologischen, wissenschaftlichen Fortschritt, zunehmende Globalisierungswirkungen, beschleunigte Urbanisierung und aber auch politischer Interferenzen sind die Veränderungsparameter eines dynamischen Geschäftsumfelds immer schnellere Transformationen ausgesetzt. Alle diese Richtungen zeigen das unsere gesellschaftliche Entwicklung inzwischen stark durch die Technik getrieben ist. Ob dies auch heißt, dass wir den Punkt der Singularität (Kurzweil) absehbar erreichen ist dennoch noch offen. ..."