The cross polar cap potential (CPCP) serves as an indicator of the energy flow into the magnetosphereionosphere system and can saturate during geomagnetic storms as the solar wind electric field increases. In this project, we investigated the uncertainties in the cross polar cap potential saturation problem by examining the differences in its estimation from different sources. We first focused on the relationship between the CPCP from different sources and their relationships with SuperMAG Auroral Electrojet (SME) and Auroral Electrojet (AE) indices to try and find different distributions. We then tried to find the relationship between CPCP values with solar wind parameters using linear regression, random forest, and multi-layer perceptron models. The parameters we use are Interplanetary Magnetic Field (IMF) components, plasma, geomagnetic index data from the OMNI database, and the SuperDARN Mapex CPCP dataset with data from 2000 to 2020. Our work showed that the CPCP has the highest Pearson correlation coefficient with the velocity, magnetic field magnitude and its vertical component, and the month compared to other drivers. Among all the models we developed, the Random Forest model performed significantly better compared to traditional regression algorithms, like Ridge, Lasso, and Elastic Net. On the basis of all model performances Neural network performed better than the Random Forest regression model with a Pearson correlation coefficient of 0.92. Similarly, the model performances displayed the same behavior for CPCP estimations made from the Polar Cap Index, however, the Pearson correlation coefficients between the predictions and actual values were higher at 0.96 for both hemispheres. Combined with the CPCP behavior for different geomagnetic activity levels, the prediction models can help shed light on the CPCP saturation problem, especially during the presence of large data gaps of external solar wind driving.

Alaska school districts have provided structured, district-led social-emotional learning since the 1990s. The Collaborative for Academic Social and Emotional Learning website showcases the Anchorage School District’s program, highlighting its standards and early initiatives as models for districts across the nation to emulate when developing and implementing their programs. Yet, K-12 social-emotional learning programs have failed to adequately support Alaskan students. Following the Covid-19 pandemic, two urban Alaskan school districts have adopted a supplementary strategy: they contract with outside agencies to provide mental health services on site at schools. Some schools have modified the social-emotional learning paradigm to increase cultural responsiveness or embed social-emotional learning in the academic curriculum. Because the writing process commonly generates fear, placing social emotional lessons in the writing curriculum provides reciprocal benefits. While, early on, social emotional learning programs modulated fear responses by regulating the autonomic nervous system preferentially via stillness or verbal expression, evidence-based techniques exist that activate the somatic nervous system in movement oriented, minimally verbal means for healing. Research by Bessel van der Kolk, Stephen Porges, Deb Dana, Peter Levine, Maggie Kline, and Kathlyn and Gay Hendricks has shown that somatic movements for coping with fear offer an effective supplement for nervous system regulation. Somatic techniques have a low threshold to participation and are collaborative, relationship-building, and minimally directive. Multiple communities’ traditional Jewish spiritual practices and philosophies act similarly, as do numerous Alaska Native pedagogies and epistemologies that have relied, for millennia, on similar theoretical principles. For novice teachers from outside Alaska, a Western social emotional learning model that includes somatic techniques may provide a bridging construct, allowing such teachers time to build cultural competence and adapt when they begin teaching in rural Alaskan communities as they develop an initial, general, culturally responsive pedagogical understanding.

Spontaneous combustion in coal mines have been a concern for miners all over the world, particularly in China, the USA and India. If allowed to establish over a large area it can become a serious environmental concern. It is important to detect it at initial stages and isolate affected coal seam to quench fire. Monitoring combustion in spoil piles of abandoned and artisanal coal mines is even more difficult due to lack of access to regulating authorities. By using Deep Learning Neural Networks, Convolutional Neural Network models can be trained to detect spontaneous combustion. These scans can be made more frequently, and detection can be done at early stages. Training dataset consists of over 5000 images processed from different mines at different conditions. The dataset is processed using Python Libraries such as TensorFlow, NumPy and Pandas to develop three different models i.e. simple CNN, LeNet and AlexNet. Accuracy comparison shows that LeNet is the most suitable model giving accuracy of 97%. It was observed that selection of an appropriate dataset is more critical than selecting model architecture.

The problem addressed by this project is the lack of high-quality children’s literature that accurately embodies Alaska Native culture, specifically Gwich’in Athabascan culture, that can be used in primary classrooms. The significance of this topic is the need for materials that Alaska Native students can connect with on a personal level and that can create a greater understanding of Native culture for all individuals. The work was completed in July of 2025. The important implications of this project are the availability of an easy-to-use storybook and connected resources to read with students and have quality discussions about Athabascan language and culture.

Metabolism was a major focus of cancer research 100 years ago, but its prevalence declined in the latter part of the twentieth century. In the last several decades, however, it has become clear that cancer cells frequently modify their metabolic programs to enable both their survival and rapid proliferation. Transaldolase (TA), an enzyme within the pentose phosphate pathway, contributes to these outcomes when its activity is increased, yet for a variety of reasons inhibition of the enzyme has remained comparatively understudied. Since TA flux enables nucleotide production and high levels of NADPH synthesis, aberrant, cancer-causing signaling pathways often increase expression of the enzyme or otherwise boost the flow of metabolites through its pathway branch. TA has also been found to take part in a synthetic lethality condition with the breast-cancer drug lapatinib, enabling the treatment to gain efficacy against otherwise drug-resistant cancer cells. TA’s neighboring enzyme on the same branch, transketolase (TK), whose reactions occur prior to and after TA’s reactions, also affects nucleotide and NADPH production, and unlike TA, drug-like inhibitors for TK have long been in existence. The recent development of a safer prodrug based on one such older TK inhibitor likely will impact developmental considerations of a TA inhibitor, as the effect of interfering with the latter’s reactions could prove somewhat redundant. Still, TA inhibition arguably will lead to distinct, potentially useful clinical effects on carcinogenesis compared to TK inhibition, reinforcing the rationale behind such research.

This project explores the influences of the personal relationships, political circumstances, and culture on the music of seven composers for flute: Friedrich Kuhlau, Carl Reinecke, Franz Doppler, George Enescu, Eldin Burton, Jonathan Cohen, and Carlos Simon, with a specific focus on eight pieces by these composers. Through this exploration it is apparent their music not only conveys their art but also embodies the composer's personal relationships, political circumstances, and culture.

This paper serves as the annotated program notes, examining the repertoire performed in my graduate piano recital on April 14th, 2024. The repertoire features Domenico Scarlatti’s Keyboard Sonatas K. 127, K. 119, K. 466, and K. 427; Johannes Brahms’s Klavierstücke Op. 119; Ludwig van Beethoven’s Sonata in A-Flat Major, Op. 110; and Franz Liszt’s Vallée d’Obermann. The official recital program and YouTube link to my recital are provided in the appendix. Each section of the paper explores the historical context, stylistic characteristics, and interpretive considerations of the compositions, highlighting their distinctive qualities and the composers’ contributions to piano literature. Key elements discussed include form, harmony, thematic connections, structural innovations, and expressive nuances, offering insights into the artistic and technical challenges these works present. This paper thus frames the recital program as a representation of diverse musical traditions and an exploration of contrasting emotional and stylistic landscapes.

This project proposes an upgrade to the Air-cooled Heat Exchangers (ACHE) known as the Intercoolers at the Central Compressor Plant located in the Prudhoe Bay oilfield on the North Slope of Alaska. The declining field is gas-limited for the majority of the year and the Intercoolers serve as a bottleneck for Field Gas Offtake (FGO). The current configuration incurs high maintenance costs, safety concerns, and operational inefficiencies. The scope of the proposed upgrade involves updating to a Tuf-Lite III fan blade, eliminating the existing gearboxes, and adding Variable Frequency Drive (VFD) control for the motor drive system. The VFDs enable the fan blades to be pitched more aggressively for optimal efficiency during the summer months. These improvements are designed to increase airflow across the bundles and thus increase the cooling capacity of the exchanger. Field testing demonstrated a 15% increase in airflow with the new blade style. The exchangers were modeled using Aspen HYSYS to estimate fouling levels and the Full Field Facility Model (FFFM) was used to correlate the increased cooling capacity of the Intercoolers with FGO. A cost-benefit analysis using HEConomics indicated a payback period of 37 months for the project. The presented study showed that the proposed upgrades are economically viable solutions for all identified problems. Implementation would occur in phases to minimize production impacts by sheltering downtime under larger planned unit outages. The primary metric for success would be the increase in airflow readings following implementation. Future optimization opportunities may include cleaning the fins or replacing the entire tube bundles.

The purpose of this master’s project is to complete a modal analysis of the Joseph E. Usibelli Engineering Learning and Innovation Building. The modal analysis was conducted in two ways: by completing a spectral analysis using data collected from 7 seismometers and using a computer model of the building’s structural Lateral Force Resisting System. Power spectrum density plots were generated using a Fast Fourier transform routine within MATLAB to identify the natural periods of the building. A building model was created in RISA-3D using construction plan sets to apply a dynamic load and then to find the natural periods of the building. Accurate results from a spectral analysis require multiple earthquakes that have a seismic input at low frequencies. The results in this report found a maximum of two fundamental modes using spectral analysis, which matched well with eigenvalues from the RISA-3D model. The advantage of the spectral analysis is that it is a direct representation of what is going on inside the building. In contrast, the dynamic analysis is used to benchmark the results.