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Solar wind driving of the cross polar cap potential: a new outlook on the saturation problem with a data-driven approachThe 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.
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Adaptive mesh refinement for variational inequalitiesVariational inequalities play a pivotal role in a wide array of scientific and engineering applications. This project presents two techniques for adaptive mesh refinement (AMR) in the context of variational inequalities, with a specific focus on the classical obstacle problem. We propose two distinct AMR strategies: Variable Coefficient Elliptic Smoothing (VCES) and Unstructured Dilation Operator (UDO). VCES uses a nodal active set indicator function as the initial iterate to a time-dependent heat equation problem. Solving a single step of this problem has the effect of smoothing the indicator about the free boundary. We threshold this smoothed indicator function to identify elements near the free boundary. Key parameters such as timestep and threshold values significantly influence the efficacy of this method. The second strategy, UDO, focuses on the discrete identification of elements adjacent to the free boundary, employing a graph-based approach to mark neighboring elements for refinement. This technique resembles the dilation morphological operation in image processing, but tailored for unstructured meshes. We also examine the theory of variational inequalities, the convergence behavior of finite element solutions, and implementation in the Firedrake finite element library. Convergence analysis reveals that accurate free boundary estimation is pivotal for solver performance. Numerical experiments demonstrate the effectiveness of the proposed methods in dynamically enhancing mesh resolution around free boundaries, thereby improving the convergence rates and computational efficiency of variational inequality solvers. Our approach integrates seamlessly with existing Firedrake numerical solvers, and it is promising for solving more complex free boundary problems.
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Healing education, nurturing through writing: autonomic & somatic nervous system social-emotional learning techniques for writing and their application in cultural translationAlaska 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.
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A Comparative analysis of diversity statements in public and private higher education institutionsThis study undertakes a comparative analysis of Diversity, Equity, and Inclusion (DEI) statements from public and private higher education institutions (HEIs) in the United States (U.S.). Employing qualitative thematic and comparative analysis, the research examines how these HEIs articulate and communicate their DEI commitments, with particular attention to the use of actionable language and communication strategies. Grounded in Social Change Theory, the study theorizes the potential role of DEI statements in fostering institutional transformation and advancing social justice. Guided by two primary research questions — (RQ1) What external factors are reflected in the language of the DEI statements of public versus private HEIs? and (RQ2) What do HEIs communicate to the public about their DEI statement on their respective institutional websites? — the study explores the influence of governance structures, political environments, and stakeholder expectations on the formulation and communication of DEI strategies. Through this analysis, the study aims to provide evidence-based recommendations for enhancing the effectiveness of DEI efforts in HEIs. In particular, the research focuses on the development of clearly defined, actionable goals and inclusive communication frameworks designed to foster accountability and catalyze social change. Ultimately, this contributes to the broader objective of promoting equity and inclusion in higher education, aligning institutional practices with the needs and expectations of diverse stakeholder groups.
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Developing a deep learning model for detection of spontaneous combustion in open pit coal minesSpontaneous 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.
