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The municipal wastewater treatment sector is undergoing a significant transformation in the transition from traditional wastewater treatment plants to resource recovery facilities. This shift, supported by policy initiatives and targeted research efforts, necessitates advances in process supervision and control to meet new demands for resource efficiency and effluent quality. Emerging factors such as an increased interest in digitalisation and the push towards a circular society further drive these advancements. However, challenges which have historically limited developments, such as harsh operating conditions for sensors and limited implementation of supervision technologies are still critical to address in this transformation. 

To advance these developments, this research explored the impact and detection of sensor and process faults within biological wastewater treatment processes using simulation-based studies with the Benchmark Simulation Model No. 1. The effects of these faults were evaluated by observing changes in operational cost, effluent quality, and controller performance. Of the tested faults, decreases in the growth rates of the autotrophic and heterotrophic bacteria most strongly affected the performance of the process. To detect and isolate the process faults of interest, sign-based fault signatures were identified from commonly available measurements, and the identified signatures were found to be capable of fault identification. For detecting sensor faults, control chart-based methods, including the Shewhart, cumulative sum, and exponentially weighted moving average (EWMA) univariate charts, as well as the multivariate EWMA chart, were applied and compared. The EWMA-based charts showed the best performance, especially in detecting slow drift faults. 

Throughout this research, emphasis was placed on reducing monitoring requirements by identifying critical measurements for effective process fault detection, and reducing potential dependencies on hardware redundancy through improved sensor fault detection. Additionally, methods that offer easy visualisation were prioritised for their potential to enhance understanding and interpretation, in hopes of facilitating the transition from research to practical application. Looking ahead, future work should investigate the ability of these methods to handle simultaneous faults and focus on their integration into full-scale systems. 

The overall aim of this summary thesis is to shed light on influential factors on children’s language, reading and writing development in didactic rooms, with a particular attention to parental influences. The summary thesis comprises three studies with aims and research questions related to the overall aim. The theoretical framework includes the Bioecological model for human development and the Person-Process-Context-Time model. In study 1, extracts from retrospective life-history oriented interviews with parents (N=27) constituted the data. In study 2, qualitative interviews with parents (N=11), all mothers, were conducted. In these two studies, thematic analyses were used. Study 1 generated nine themes. These themes reflect influential factors on children’s language, reading and writing development at home, in preschool and preschool class: Child’s abilities and engagement; Genetics and parents’ abilities; More able siblings and peers; Involved parents and grandparents; Teacher competence, attitude and collaborations; Social climate in preschool and preschool class; Free play and child-initiated reading and writing activities; Toys and books; and Extra support provisions and stimulation. Study 2 generated six themes reflecting influential factors on children’s language, reading and writing development, as well as general parental support to children in school grade 1: Talk with children about school; Handle school-related problems; Have a positive attitude towards school; Guide children’s reading and writing; Coach specific training; and Collaborate with school staff about children’s reading homework. In study 3, research articles (N=106) were identified and included in a literature review. The review had nine interconnected phases and comprised a qualitative inductive content analysis. Study 3 generated 11 categories which constitute valuable reading aloud sessions at home, in preschool and school: Choose a place, book and extra material; Ensure appropriate group constellation; Support transition and create a routine; Consider when, how often and for how long; Communicate expectations and encourage engagement; Read in an appealing way and provide explanations; Include dialogues; Teach how to read; Encourage cosiness and empathy; Incorporate movement and play; and Cooperate with one another. The synthesis of the results reveals through which interactions, with which people, with which objects, in which settings and occurring situations, and during which time, children’s language, reading and writing development are being influenced. In all three studies, guiding principles set by the Swedish Research Council have been followed. Influential factors in didactic rooms at home, in preschool and school, as well as implications, limitations and suggestions for further research, are discussed. The summary thesis has relevance for parents, teachers in preschool and school, and politicians, as well as others interested in children’s language, reading and writing development. 

Real-time systems such as industrial robots and automated guided vehicles integrate a wide range of algorithms with varying levels of timing requirements to achieve their functional behavior. Historically, in certain systems, these algorithms were deployed on dedicated single-core hardware platforms that exchanged information over a real-time network, while more recent designs have adapted an integrated architecture where these algorithms are executed on an embedded multi-core hardware platform. The advantages provided by cloud and fog architectures for non-real-time applications have prompted discussions around the possibility of achieving similar advantages for systems such as industrial robot controllers by moving from an embedded architecture to a cloud and fog native architecture. This thesis addresses a subset of challenges related to scheduling to facilitate this transition and presents three main contributions aimed at improving online scheduling methodologies in multi-server systems for applications with real-time requirements. First, an approach based on minimum parallelism reservations is proposed for scheduling sequential tasks in hierarchical multi-server systems with clairvoyant inputs, ensuring adherence to hard real-time requirements. Second, a framework is introduced that utilizes estimated processing times to enhance average throughput in distributed multi-queue multi-server systems while managing tasks with stochastic inputs and firm real-time requirements, thereby improving resource utilization. Finally, competitive algorithms are proposed that leverage estimated processing times to minimize average (modified) tardiness in centralized single-queue multi-server systems, addressing the scheduling of sequential tasks with arbitrary arrivals and soft real-time requirements. Collectively, these contributions establish a robust foundation for improving the performance of real-time systems operating in increasingly complex environments characterized by dynamic workloads and varying resource availability.

This work explores the design and optimization of electrified configurations in aviation, focusing on the application and adaptation of these technologies across various aircraft classes. It utilizes a range of methodologies, including the development of a novel engine design approach, multi-disciplinary frameworks and the integration of surrogate modeling techniques, to enhance the conceptual design process and facilitate efficient exploration of complex design spaces.

This work demonstrates that electrified systems can significantly improve fuel efficiency and reduce emissions, particularly in short-haul applications where the current limitations of battery technology are less restrictive. The work identifies critical design trade-offs, such as the impact of battery weight on overall aircraft performance and the benefits of distributed propulsion systems in reducing aerodynamic drag and enhancing energy efficiency.

An uncertainty analysis further reveals the critical role of technological advancements in electrical powertrain components and their implications for the operational viability of electrified aircraft. The findings indicate that while near-term benefits can be achieved with current hybrid configurations, fully electrified aircraft will depend on future improvements in battery energy density and powertrain technologies.

Through these diverse methodologies and analyses, this work contributes to a deeper understanding of the challenges and opportunities in electrified aircraft design, offering insights that are crucial for advancing sustainable aviation.

This thesis presents advancements in the valuation and modeling of financial derivatives, with a focus on American and Bermudan options. Traditional models such as Black–Scholes assume constant volatility, often leading to inaccurate pricing during periods of high market turbulence. This research addresses these limitations by considering more flexible regime–switching and stochastic volatility models.

The first part of the thesis focuses on the pricing of American options under a Markovian regime–switching model, extending previous works by addressing assumptions of asset returns across economic states. By incorporating the Totally Positive of Order 2 (TP2) property for Transition Probability Matrix (TPM) and Conditional Probability Matrix (CPM), the model ensures probabilistic progression between economic states. Extensive numerical experiments confirm the importance of TPM in maintaining the monotonicity of optimal exercise boundaries.

Secondly, the thesis investigates asymptotic expansions of implied volatility under the Gatheral model. Numerical analysis reveals the accuracy of first and second–order expansions, with a partial calibration method validated using market data from the COVID–19 pandemic.

Next, the thesis introduces a Backward Stochastic Differential Equation (BSDE)–θ scheme for pricing American options under the Heston model, simplifying the computational process and requiring only one parameter for pricing while also deriving schemes for Delta and Vega hedging strategies. Extensive numerical experiments validate the scheme’s accuracy and robustness, especially for in–the–money options.

Finally, the thesis develops an Almost–Exact Simulation (AES) scheme for Bermudan and American option pricing under Heston–type models. The AES scheme ensures non–negative variance and significantly improves simulation accuracy compared to the Euler scheme when the number of steps equals the number of exercise dates. Numerical experiments reveal that the AES scheme offers improvements in accuracy, efficiency, and memory usage, particularly for in–the–money and at–the–money options with minimal time steps.