The enactment of the NCLB Act of 2001 and its legislative mandates for accountability testing throughout the nation brought to the forefront the issue of data-driven decision making. This emphasis on improving education has been spurred due to the alleged failure of the public school system. As a result, the role of administrators has evolved to incorporate data-driven decision-making practices to help make educational choices. While the underlying assumption of implementing data-driven decision making is that it will lead to improvements in education, this has yet to be empirically proven. The purpose of the study was to analyze the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. This census study addressed principals of k-5 public elementary schools. In this quantitative study, a web-based survey was used to measure principals' data-driven ion-making practices. The student achievement data examined were the California Standards Test results for English language arts and mathematics for the 2009–2010 and 2010–2011 school years. Through a series of multiple regression analyses, the study examined the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. Specifically. this study explored the amount of variance in student achievement scores in language arts and mathematics that could be explained by school characteristics, principals' level of experience, and data-driven decision-making practices. The results showed principals are incorporating data-driven decision-making practices in k-5 public elementary schools in California. In addition, the results showed that principals believe the quality of their decision making has improved due to implementing data-driven decision making. Principals indicated they were incorporating practices identified in the four constructs used in the present study: (a) establishing a data-driven culture, (b) data-driven decision making by teachers to improve student achievement, (c) supporting systems for DDDM, and (d) collaboration among teachers using data-driven decision making. A strong negative correlation was found between the number of students on free and reduced lunch and student achievement.

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The purpose of the study was to find out what challenges governments have encountered when implementing Artificial Intelligence.  The method used was qualitative and the interviews were conducted remotely. Four governments were interviewed where respondents were asked questions about what they had experienced as challenges in the implementation of AI. The results were analyzed against previous studies and compiled by picking out themes from the transcribed interviews.  The results of the survey identify a number of challenges linked to three main themes: the lack of knowledge, challenges around data and when challenges arise.

Developing and improving upon a good empirical model for an engine can be time-consuming and costly. The goal of this thesis has been to evaluate data-driven modelling, specifically neural networks, to see how well it can handle training for some static models like the mass flow of air into the cylinder, mean effective pressure and pump mean effective pressure but also for transient modelling, specifically the exhaust gas temperature. These models are evaluated against the classical empirical models to see if neural networks are a viable modelling option. This is done with five different types of neural networks which are trained. These are the feed-forward neural network, Nonlinear autoregressive exogenous model network, layer recurrent network, long short term memory network and gated recurrent network.The inputs were determined by looking at more simple physical models but also looking at the covariance to determine the usefulness of the input. If the calculation time is small for the specific network, the neural network structure is tested and optimized by training many networks and finding the median/mean result for that specific test.The result has shown that the static models are handled very well by the most simple feed-forward network. For the exhaust temperature, both NARX and Layer recurrent network could predict and handle it well giving results very close to the empirical models and could be a viable option for transient modelling, on the other hand, Long short term memory, gated recurrent network and the feed-forward network had trouble predicting the exhaust gas temperature and returned bad results while training.

Nuclear power plants are inherently complex systems. While the technology has been used to generate electrical power for many decades, process monitoring continuously evolves. There is always room for improvement in terms of maximizing the availability by reducing the risks of problems and errors. In this context, automated monitoring systems have become important tools – not least with the rapid progress being made in the field of data analytics thanks to ever increasing amounts of processing power. There are many different types of models that can be utilized for identifying anomalies. Some rely on physical properties and theoretical relations, while others rely more on the patterns of historical data. In this thesis, a data-driven approach using a hierarchical autoencoder framework has been developed for the purposes of anomaly detection at the Swedish nuclear power plant Forsmark. The model is first trained to recognize normal operating conditions. The trained model then creates reference values and calculates the deviations in relation to real data in order to identify any issues. This proof-of-concept has been evaluated and benchmarked against a currently used hybrid model with more physical modeling properties in order to identify benefits and drawbacks. Generally speaking, the created model has performed in line with expectations. The currently used tool is more flexible in its understanding of different plant states and is likely better at determining root causes thanks to its physical modeling properties. However, the created autoencoder framework does bring other advantages. For instance, it allows for a higher time resolution thanks to its relatively low calculation intensity. Additionally, thanks to its purely data-driven characteristics, it offers great opportunities for future reconfiguration and adaptation with different signal selections.

Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.

Smart cities embrace cutting-edge technologies to improve transportation efficiency and safety. With the rollout of 5G and an ever-growing network of connected infrastructure sensors, real-time road condition awareness is becoming a reality. However, this progress brings new challenges. The communication and vast amounts of data generated by autonomous vehicles and the connected infrastructure must be navigated. Furthermore, different levels of autonomous driving (ranging from 0 to 5) are rolled out gradually and human-driven vehicles will continue to share the roads with autonomous vehicles for some time. In this work, we apply a data-driven control scheme called Learning Model Predictive Control (LMPC) to three different smart city scenarios of increasing complexity. Given a successful execution of a scenario, LMPC uses the trajectory data from previous executions to improve the performance of subsequent executions while guaranteeing safety and recursive feasibility. Furthermore, the performance from one execution to another is guaranteed to be non-decreasing. For our three smart-city scenarios, we apply a minimum time objective and start with a single vehicle in a two-lane intersection. Then, we add an obstacle on the lane of the ego vehicle, and lastly, we add oncoming traffic. We find that LMPC gives us improved traffic efficiency with shorter travel. However, we find that LMPC may not be suitable for real-time training in smart city scenarios. Thus, we conclude that this approach is suitable for simulator-driven, offline, training on any trajectory data that might be generated from autonomous vehicles and the infrastructure sensors in future smart cities. Over time, this can be used to construct large data sets of optimal trajectories which are available for the connected vehicles in most urban scenarios. / Smarta städer använder modern teknik för att förbättra transporteffektiviteten och säkerheten. Med införandet av 5G och ett allt större nätverk av uppkopplade sensorsystem för infrastruktur blir realtidsmedvetenhet om vägförhållandena en verklighet. Denna utveckling medför nya utmaningar. Kommunikationen mellan autonoma fordon och uppkopplade sensorsystem ger upphov till stora mängder data som måste hanteras. Dessutom kommer fordon med olika autocnominivåer (från 0 till 5) att behöva dela gatorna tillsammans med människostyrda fordon samtidigt under en tid. I detta arbete tillämpar vi en datadriven reglermetod som heter Learning Model Predictive Control (LMPC) på tre olika scenarier i en smart stad med ökande komplexitet. LMPC utnyttjar data från en tidigare lyckad körning av ett visst scenario för att förbättra prestandan på efterföljande körningar samtidigt som säkerheten och rekursiv genomförbarhet garanteras. Vidare garanteras att prestandan från en körning till en annan inte minskar. För våra tre scenarier är målet att minimerar restiden och börjar med ett enda fordon i en tvåfilig korsning. Sedan lägger vi till ett hinder på högra filen och till sist lägger vi till mötande trafik. Vi finner att LMPC ger oss förbättrad trafikeffektivitet med kortare restid. Vi finner dock att LMPC må vara mindre lämplig för realtids scenarier. Således drar vi slutsatsen att denna metod är lämplig för optimering i simulatorer, offline, på data som kan genereras från autonoma fordon och sensorsystemet i infrastrukturen. Så småningom kan vår metod användas för att konstruera stora dataset av optimala trajektorier som är tillgängliga för uppkopplade fordon i framtidens smarta städer.

In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). The simulations using these ML potentials are then compared with results obtained from ab-initio simulations using the gold standard method of density functional theory (DFT), as implemented in the Vienna ab-intio simulation package (VASP). The results show that the MTP simulations reach comparable accuracy compared to the DFT simulations for the properties total energy and MSD for Aluminium, with errors in the orders of magnitudes of meV and 10-5 Å2. Specific heat capacity is not reasonably replicated for Aluminium. The MTP simulations do not reasonably replicate the studied properties for the system of Silicon. The KRR models are implemented in the most direct way, and do not yield reasonably low errors even when trained on all available 10000 time steps of DFT training data. On the other hand, the MTPs require only to be trained on approximately 100 time steps to replicate the physical properties of Aluminium with accuracy comparable to DFT. After being trained on 100 time steps, the trained MTPs achieve mean absolute errors in the orders of magnitudes for the energy per atom and force magnitude predictions of 10-3 and 10-1 respectively for Aluminium, and 10-3 and 10-2 respectively for Silicon. At the same time, the MTP simulations require less core hours to simulate the same amount of time steps as the DFT simulations. In conclusion, MTPs could very likely play a role in accelerating both materials simulations themselves and subsequently the emergence of the data-driven materials design and informatics paradigm.

When boring machines are used, large loads are exerted on their structure. The load cycles cause material fatigue on the boring machine structure. If the material fatigue can be estimated in real-time, maintenance can be planned more efficiently and the effect of different types of usage can be evaluated. Because of the many advantages of knowing the material fatigue, the goal of this thesis is to develop a model to predict the strain of a boring machine structure and then derive an estimate of the material fatigue caused by the strain. To do this several approaches using machine learning techniques are evaluated. The input signals were selected using both coherence analysis and mutual information. It was found that linear models outperform the tested non-linear model structures, and that non-linear mechanical connections cause difficulties. The signals to be modelled contained high frequency components that were not present in the available input signals. The results show that given favorable sensor positions, an estimate of the material fatigue can be made with sufficient accuracy when using a noise model and noise realization to cover the non-existent high frequency components.

This dissertation develops systems engineering and machine learning models for aviation maintenance support. With the constant increase in demand for air travel, aviation organizations compete to maintain airworthy aircraft to ensure the safety of passengers. Given the importance of aircraft safety, the aviation sector constantly needs technologies to enhance the maintenance experience, ensure system safety, and limit aircraft downtime. Based on the current literature, the aviation maintenance sector still relies on outdated technologies to maintain aircraft maintenance documentation, including paper-based technical orders. Aviation maintenance documentation contains a mixture of structured and unstructured technical text, mainly inputted by operators, making them prone to error, misunderstanding communication, and inconsistency. This dissertation intends to develop decision support models based on systems engineering and artificial intelligence models that can automate the maintenance documentation system, extract useful information from maintenance work orders, and predict the aircraft's top degrader signals based on textual data. The first chapter of this dissertation introduces the significant setbacks of the aviation industry and provides a working ground for the following chapters. The dissertation's second chapter develops a system engineering framework using model-based systems engineering (MBSE) methodology to model the aviation maintenance process using the systems engineering language (SysML). The outcome of this framework is the design of an automated maintenance system model that can be used to automate maintenance documentation, making it less prone to error. The third chapter of the dissertation uses textual data in maintenance work orders to develop a hybrid approach that uses natural language processing (NLP) and transformer models to predict the readiness of a legacy aircraft. The model was tested using a real-life case study of the EA-6B military aircraft. The fourth chapter of this dissertation develops an ensemble transformer model based on three different transformer models. The ensemble model leverages the benefits of three different transformer architectures and is used to classify events based on an aviation log-based dataset. This dissertation's final and fifth chapter summarizes key findings, proposes future work directions, and provides the dissertation's limitations.