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The purpose of this study was to examine how race and gender bias influence music educators' perceptions of musical style and evaluations of brief jazz and classical piano performances. Previous research has shown that race and gender bias and stereotype activation influence our judgment of others. These factors could result in biased evaluations of musical performances, including ensemble auditions and college level juries. I constructed an instrument designed to test these biases by experimentally manipulating race and gender variables of jazz and classical performances. Videos of a black male, white male, white female, and black female pianist were synchronized with identical audio recordings to control for performer ability. The first experiment measured how stereotypes influence participants' proper identification of jazz and classical styles in a series of 2-second video clips. The second experiment measured how race and gender bias influence participants' evaluations of jazz and classical performances in a series of 10-second video clips. The participants in this study were a national sample of applied music faculty (n=315). Participants were randomly assigned to four test conditions in a 2x2 (performer race X performer gender) between subjects blind experimental design. The dependent variables were classical jury grade predictions, jazz jury grade predictions, and accuracy of style identification. Results of a 2x2 ANOVA revealed significant differences in style identification by gender and interaction of race and gender. Participants were more likely to associate female performers with classical music and the black male performer with jazz. There were also significant differences in classical jury grade predictions by race, and jazz jury grade predictions by the interaction of race and gender. The black male performer received the lowest average jury grade predictions in both jazz and classical performances, scoring between 0.5 and 1 letter grade lower than the other performers. Results suggest that a negative association of females and jazz music still exists, as well as a stereotype of a black male jazz performer. While females did not receive significantly lower jazz jury grade predictions than the male performers, they may still feel marginalized in college jazz programs and ensembles. The results also suggest that black males may be at a significant disadvantage in college music admissions, auditions, and juries. These results have serious implications for music educators at every level. We must strive for fair and equitable audition processes and ensure that every child, regardless of race or gender, has an equal opportunity to participate in ensembles and music programs. / Music Education

Fifth Generation (5G) introduces technologies that expedite the adoption of mobile networks, such as densely connected devices, ultra-fast data rate, low latency and more. With those visions in 5G and 6G in the next step, the need for a higher transmission rate and lower latency is more demanding, possibly breaking Moore’s law. With Artificial Intelligence (AI) techniques becoming mature in the past decade, optimizing resource allocation in the network has become a highly demanding problem for Mobile Network Operators (MNOs) to provide better Quality of Service (QoS) with less cost. This thesis proposes a Reinforcement Learning (RL) solution on bandwidth allocation for network slicing integration in disaggregated Open Radio Access Network (O-RAN) architecture. O-RAN redefines traditional Radio Access Network (RAN) elements into smaller components with detailed functional specifications. The concept of open modularization leads to greater potential for managing resources of different network slices. In 5G mobile networks, there are three major types of network slices, Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Each network slice has different features in the 5G network; therefore, the resources can be relocated depending on different needs. The virtualization of O-RAN divides the RAN into smaller function groups. This helps the network slices to divide the shared resources further down. Compared to traditional sequential signal processing, allocating dedicated resources for each network slice can improve the performance individually. In addition, shared resources can be customized statically based on the feature requirement of each slice. To further enhance the bandwidth utilization on the disaggregated O-RAN, a RL algorithm is proposed in this thesis on midhaul bandwidth allocation shared between Centralized Unit (CU) and Distributed Unit (DU). A Python-based simulator has been implemented considering several types of mobile User Equipment (UE)s for this thesis. The simulator is later integrated with the proposed Q-learning model. The RL model finds the optimization on bandwidth allocation in midhaul between Edge Open Cloud (O-Cloud)s (DUs) and Regional O-Cloud (CU). The results show up to 50% improvement in the throughput of the targeted slice, fairness to other slices, and overall bandwidth utilization on the O-Clouds. In addition, the UE QoS has a significant improvement in terms of transmission time.

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Die geometrischen Abmessungen von Lebensmitteln werden auf eine für den Verzehr geeignete Größe reduziert. Für diesen Vorgang wird oft das Zerschneiden der Lebensmittel zur Anwendung gebracht. Neben der manuellen Ausführung des Schneidens durch den Endkonsumenten oder den Betrieb des Lebensmittelhandwerks wird in der industriellen Konsumgüterproduktion das Schneiden mit hohen Geschwindigkeiten und relativ hoher Ausbringung von Maschinen umgesetzt. Mit den hohen Verarbeitungsgeschwindigkeiten gehen Herausforderungen einher, die vor allem auf das visko-elastische Verhalten der Lebensmittel und das geschwindigkeitsabhängige Verhalten der Wirkpaarung aus Messer und Schneidgut zurückzuführen sind. Auftretende Probleme sind Oberflächenschäden an der Schnittfläche in Form von Ausbrüchen, Deformationen und im Extremfall Denaturierungen. Für die Deformation und die Denaturierung ist das Reibungsverhalten der Wirkpaarung entscheidend, weshalb in dieser Arbeit eine Methode zur besseren Charakterisierung des Reibungsverhaltens beim Schneiden entwickelt und in Experimenten zur Anwendung gebracht wird. Als Basis wird ein Modell zur Abbildung der relevanten Größen entworfen, welches auf Modellen aus dem Schneiden von Lebensmitteln und Metallen aufbaut. Zur Anwendung des Modells wird ein Ablauf zur Auswertung von Schneidkraftverläufen entwickelt. Zur Prüfung der Methode werden Schneidversuche mit verschiedenen Geschwindigkeiten, Messerbeschichtungen und Schneidgütern durchgeführt. Aus Permutationen dieser Versuchsparameter gehen verschiedene Wirkpaarungen hervor, für deren Schneidkraftverläufe die entwickelte Methode zur Anwendung gebracht wird. Im Ergebnis stehen Kennwerte jedes durchgeführten Schnitts, welche das Schneidverhalten charakterisieren, zur Verfügung. In genormten Experimenten werden mechanische Kennwerte der Schneidgüter und Reibungskennwerte der Messeroberflächen bestimmt und mit den Kennwerten der Methode statistisch abgeglichen. Abschließend wird die entwickelte Methode in Bezug zu den genormten Kenngrößen diskutiert und die Bedeutung für die Verarbeitungstechnik eingeordnet. / The geometric dimensions of foods need to be reduced to a size suitable for consumption. Cutting of food is often brought to application for this purpose. In addition to the manual execution of cutting by the consumer, cutting is implemented in industrial consumer goods production at high speeds and relatively high output rates of the machines. The high processing speeds are accompanied by challenges, which are mainly due to the visco-elastic behavior of the food and the speed-dependent behavior of the active pairing consisting of knife and material to be cut. Emerging problems are surface damage on the cut surface in the form of chipping, distortion and denaturation. The frictional behavior of the active pairing is decisive for the deformation and denaturation. Therefore a method for better characterization of the frictional behavior during cutting is developed in this work and applied in experiments. A model representing the relevant variables builds the basis, which is derived from models for cutting of foodstuffs and metals. A procedure for evaluating cutting force curves is developed in order to apply the model. Cutting tests with different speeds, knife coatings and cutting materials are carried out to test the newly developed method. The permutations of these test parameters give rise to various active pairings. For cutting force curves of these generated pairings the developed method is applied. In the results characteristic values of each performed cut are presented. They characterize the cutting behavior of the active pairing. Mechanical parameters of the cutting materials and friction parameters of the knife surfaces are determined in accompanying standardized experiments and statistically compared with the parameters of the method. Finally, the developed method and the significance for processing technology are discussed in relation to the standardized parameters.

The fifth-generation (5G) of mobile communication networks are expected to support a large number of vertical industries requiring services with diverging requirements. To accommodate this, mobile networks are undergoing a significant transformation to enable a variety of services to coexist on the same infrastructure through network slicing. Additionally, the introduction of distributed user-plane and multi-access edge computing (MEC) technology allows the deployment of virtualised applications close to the network edge. The first part of this dissertation focuses on end-to-end network slice provisioning for various vertical industries with different service requirements. Two slice provisioning strategies are explored, by formulating a mixed integer linear programming (MILP) problem. Further, a genetic algorithm (GA)-based approach is proposed with the aim to improve search-space exploration. Simulation results show that the proposed approach is effective in providing near-optimal solutions while drastically reducing computational complexity. In a later stage, the study focuses on building a measurement-based digital twin (DT) for the highly heterogeneous MEC ecosystem. The DT operates as an intermediate and collaborative layer, enabling the orchestration layer to better understand network behavior before making changes to the physical network. Assisted by proper AI/ML solutions, the DT is envisioned to play a crucial role in automated network management. The study utilizes an emulated and physical test-bed to gather network key performance indicators (KPIs) and demonstrates the potential of graph neural network (GNN) in enabling closed loop automation with the help of DT. These findings offer a foundation for future research in the area of DT models and carbon footprint-aware orchestration.