Vehicle velocity distributions are of utmost relevance for the efficiency, safety, and sustainability of road traffic. Yet, due to technical limitations, they are often empirically analyzed using spatiotemporal averages. Here, we instead study a novel set of microscopic traffic data from Dresden comprising 346 million data points with a resolution of one vehicle from 145 detector sites with a particular focus on extreme events and distribution tails. By fitting q-exponential and Generalized Extreme Value distributions to the right flank of the empirical velocity distributions, we establish that their tails universally exhibit a power-law behavior with similar decay exponents. We also find that q-exponentials are best suitable to model the vast extent to which speed limit violations in the data occur. Furthermore, combining velocity and time headway distributions, we obtain estimates for free flow velocities that always exceed average velocities and sometimes even significantly exceed speed limits. Likewise, congestion effects are found to play a very minor, almost negligible role in traffic flow at the detector sites. These results provide insights into the current state of traffic in Dresden, hinting toward potentially necessary policy amendments regarding road design, speed limits, and speeding prosecution. They also reveal the potentials and limitations of the data set at hand and thereby lay the groundwork for further, more detailed traffic analyses.

In applications spanning across aerospace, marine, automotive, energy, and space travel domains, composite materials have become ubiquitous because of their superior stiffness-to-weight ratios as well as corrosion and fatigue resistance. However, from a manufacturing perspective, these advanced materials have introduced new challenges that demand the development of new tools. Due to the complex anisotropic and nonlinear material properties, composite materials are more difficult to model than conventional materials such as metals and plastics. Furthermore, there exist ultra-high precision requirements in safety critical applications that are yet to be reliably met in production. Towards developing new tools addressing these challenges, this dissertation aims to (i) build high-fidelity numerical simulations of composite assembly processes, (ii) bridge these simulations to machine learning tools, and (iii) apply data-driven solutions to process control problems while identifying and overcoming their shortcomings. This is accomplished in case studies that model the fixturing, shape control, and fastening of composite fuselage components. Therein, simulation environments are created that interact with novel implementations of modified proximal policy optimization, based on a newly developed reinforcement learning algorithm. The resulting reinforcement learning agents are able to successfully address the underlying optimization problems that underpin the process and quality requirements. / Doctor of Philosophy / Within the manufacturing domain, there has been a concerted effort to transition towards Industry 4.0. To a large degree, this term refers Klaus Schwab's vision presented at the World Economic Forum in 2015, in which he outlined fundamental systemic changes that would incorporate ubiquitous computing, artificial intelligence (AI), big data, and the internet-of-things (IoT) into all aspects of productive activities within the economy. Schwab argues that rapid change will be driven by fusing these new technologies in existing and emerging applications. However, this process has only just begun and there still exist many challenges to realize the promise of Industry 4.0. One such challenge is to create computer models that are not only useful during early design stages of a product, but that are connected to its manufacturing processes, thereby guiding and informing decisions in real-time. This dissertation explores such scenarios in the context of composite structure assembly in aerospace manufacturing. It aims to link computer simulations that characterize the assembly of product components with their physical counterparts, and provides data-driven solutions to control problems that cannot typically be solved without tedious trial-and-error approaches or expert knowledge.

This thesis focuses on how cultural connections in classroom influences students value of learning, specifically, their value of reading. Several researchers and theorists have emphasized the importance of balancing cognitive and conative aspects of childrens reading development. However, what is lacking in these studies is a focus on Pacific Islander (PI) children. The purpose of this study was to examine value of reading for diverse students who may be struggling in classrooms designed for White, middle-class students. Findings provide educators and those working with diverse students a chance to consider how connecting cultural backgrounds for all students can help in classrooms. This study was framed from a larger study on equity in teaching academic language conducted by the supervising professor, Dr. Bryant Jensen. This research study used a mixed method approach: multiple regression analysis to predict gains in PI students reading values, and interviews with classroom teachers. Fourth through sixth grade Latino and PI students in 32 classrooms participating in the quantitative study, and three teachers were interviewed. Due to the short time frame, PI students value of reading did not increase on average. Themes also emerged during interviews with the classroom teachers. I conclude with a discussion, implications, and recommendations for future research studies and educators working with PI and other diverse students.

A key distinction between today's and tomorrow's wireless networks is the appetite for reliability to enable emerging mission-critical services such as ultra-reliable low-latency communication (URLLC) and hyper-reliable low-latency communication (HRLLC), the staple mission-critical services in IMT-2020 (5G) and IMT-2023 (6G), for which reliable and resilient communication is a must. However, achieving ultra-reliable communication is challenging because of these services' stringent reliability and latency requirements and the stochastic nature of wireless networks. A natural way of increasing reliability and reducing latency is to provision additional network resources to compensate for uncertainty in wireless networks caused by fading, interference, mobility, and time-varying network load, among others. Thus, an important step to enable mission-critical services is to identify and quantify what it takes to support ultra-reliable communication in mobile networks -- a process often referred to as dimensioning. This dissertation focuses on resource dimensioning, notably spectrum, for ultra-reliable wireless communication. This dissertation proposes a set of methods for spectrum dimensioning based on concepts from risk analysis, extreme value theory, and meta distributions. These methods reveal that each ``nine'' in reliability (e.g., five-nines in 99.999%) roughly translates into an order of magnitude increase in the required bandwidth. In ultra-reliability regimes, the required bandwidth can be in the order of tens of gigahertz, far beyond what is typically available in today's networks, making it challenging to provision resources for ultra-reliable communication. Accordingly, this dissertation also investigates alternative approaches to provide resources to enable ultra-reliable communication services in mobile networks. Particularly, this dissertation considers multi-operator network sharing and multi-connectivity as alternatives to make additional network resources available to enhance network reliability and proposes multi-operator connectivity sharing, which combines multi-operator network sharing with multi-connectivity. Our studies, based on simulations, real-world data analysis, and mathematical models, suggest that multi-operator connectivity sharing -- in which mobiles multi-connect to base stations of operators in a sharing arrangement -- can reduce the required bandwidth significantly because underlying operators tend to exhibit characteristics attractive to reliability, such as complementary coverage during periods of impaired connectivity, facilitating the support for ultra-reliable communication in future mobile networks. / Doctor of Philosophy / A key distinction between today's and tomorrow's wireless networks is the appetite for reliability to enable emerging mission-critical services in 5G and 6G, for which ultra-reliable communication is a must. However, achieving ultra-reliable communication is challenging because of these services' stringent reliability and latency requirements and the stochastic nature of wireless networks. Reliability often comes at the cost of additional network resources to compensate for uncertainty in wireless networks. Thus, an important step to enable ultra-reliable communication is to identify and quantify what it takes to support mission-critical services in mobile networks -- a process often denoted as dimensioning. This dissertation focuses on spectrum dimensioning and proposes a set of methods to identify suitable spectrum bands and required bandwidth for ultra-reliable communication. These methods reveal that the spectrum needs for ultra-reliable communication can be beyond what is typically available in today's networks, making it challenging to provide adequate resources to support ultra-reliable communication services in mobile networks. Alternatively, we propose multi-operator connectivity sharing: mobiles simultaneously connect to multiple base stations of different operators. Our studies suggest that multi-operator connectivity sharing can reduce the spectrum needs in ultra-reliability regimes significantly, being an attractive alternative to enable ultra-reliable communication in future mobile networks.

No description available.

Notwithstanding the negative influence of demotivation on student learning outcomes, prior research in EFL demotivation suffers from the lack of generally agreed-upon conceptual understanding, which hampers scale development. The present series of studies sought to explore the ideas of demotivation and describe the development of the Learner Perception of Demotivators Scale (LPDS) both conceptually and psychometrically. In Study 1 (N = 295), an exploratory factor analysis offered preliminary support for a factor structure comprising three dimensions: negative teacher behavior, loss of task value, and low expectancy for success. In Study 2 (N = 320), the proposed factor structure was further corroborated through confirmatory factor analysis, and its validity was documented by means of correlating with academic performance, self-efficacy, and mindset. A second-order factor model was tested to investigate whether a set of demotivating factors load on an overall construct that may be termed “Demotivator”. Whereas the model fit confirmed a wellitting second-order model with post hoc model adjustment, one low first-order loading (negative teacher behavior) does not seem to support “Demotivator” as a higher order construct comprising three subdimensions. Furthermore, the LPDS demonstrated evidence of configural, metric, scalar, and residual invariance across gender, suggesting the same underlying construct is measured across gender groups. Contrary to the findings in motivation research, loss of task value was a stronger predictor of performance than low expectancy for success. Further, in Study 3 (N =320), loss of task value distinguished extremely motivated EFL learners from ordinary ones, offering tentative evidence for the reason behind demotivation in EFL learning. The unique role of task value found in Study 2 and Study 3 gave insights into the hypothetical construct of “demotivation”. It was also examined in the context of East Asian culture. By establishing a nomological network (academic performance, self-efficacy, and mindset), the current study provided a lawful pattern of interrelationships that exists between the hypothetical construct (demotivation) and observable attributes (e.g., academic performance) and that guides researcher for future L2 studies. More implications and limitations for future studies are discussed.

No description available.

No description available.

No description available.

Fundamental engineering courses are important to the undergraduate engineering student experience but have been associated with challenging educational environments. Several factors influence the educational environment, although learning experiences are primarily the outcome of interactions between instructors and students. To initiate change, it is important to understand teaching and learning experiences in fundamental engineering courses from the perspectives of the key players in these environments: instructors and students. To accomplish the goal of understanding teaching and learning experiences, I conducted studies that examined instructors' and students' perspectives on their experiences and the educational environments, using qualitative research methodology. Through these studies, this dissertation: 1) examined instructors' beliefs and self-described behaviors, guided by motivation theory and focusing on the role of instructors as socializers in the learning process; 2) considered interacting fundamental engineering courses as a foundational curriculum within engineering curricula to describe the educational environment in these courses from instructors' perspectives; and 3) examined student perceptions of their learning experiences and the educational environments in fundamental engineering courses using responses to open-ended items in end-of-semester student evaluations of teaching surveys. Data indicate that participants strive to integrate strategies that promote effective learning despite challenges posed by course environments, although expected gains from these behaviors may not always be maximized. Students and instructors may benefit from a student-focused, collaborative and holistic course planning process that considers interacting fundamental courses as a foundational curriculum within engineering curricula, and that engages instructors as equal partners in the planning process. Student feedback may be infused into the course planning process by productively and meaningfully utilizing students' responses to end-of-semester student evaluations of teaching surveys. Overall, the results of this dissertation highlight the importance of institutional support, collaboration, and integrating student feedback in the quest for facilitating effective educational environments and positive learning experiences in engineering. / Doctor of Philosophy / Introductory engineering courses are important to engineering students’ college experience but have been associated challenging learning environments. Several factors influence the learning environment, although learning experiences are primarily the outcome of interactions between instructors and students. To initiate change, it is important to understand teaching and learning experiences in introductory engineering courses from the points of view of the key players in these environments: instructors and students. To accomplish the goal of understanding teaching and learning experiences, I conducted qualitative studies that examined instructors’ and students’ points of view on their experiences and the learning environments. Through these studies, this dissertation: 1) examined instructors’ beliefs and self-described behaviors, guided by motivation theory and focusing on the role of instructors as socializers in the learning process; 2) considered interacting introductory engineering courses as a foundational curriculum within engineering curricula to describe the learning environment in these courses from instructors’ points of view; and 3) examined student perceptions of their learning experiences and environments in introductory engineering courses using responses to open-ended items in end-of-semester student evaluations of teaching surveys. Results show that participants strive to integrate strategies that promote effective learning despite challenges posed by learning environments, although the expected benefits from these strategies may not always be realized. Students and instructors may benefit from a student-focused, collaborative and holistic course planning process that considers interacting introductory engineering courses as a foundational curriculum within engineering curricula, and that involves v instructors as equal partners in the planning process. Student feedback may be included in the course planning process by productively and meaningfully using students’ responses to end-of-semester student evaluations of teaching surveys. Overall, the results of this dissertation highlight the importance of institutional support, collaboration, and integrating student feedback in the quest for facilitating effective learning environments and positive learning experiences in engineering.