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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
The Radical Relationality Of Complex Partnerships: Community-Member Experiences In Critical Community-Based Learning, Amie Riley
Dissertations and Theses
Through a radical relationality within the social-ecological systems that sustain us, critical community-based learning (CBL) in higher education offers a praxis for engaging the demanding pedagogical and community challenges we face. When CBL is implemented as both a critical and sustainability pedagogy, as a strategy for social change, the relationships created by CBL partnerships have the potential to generate transformational outcomes for all partnership agents. Using a critical complexity theoretical framework, a bricolage of complexity science and critical theory, this critical qualitative study sought to understand the systemic patterns and behaviors of a community-based learning partnership by elevating community-member voices. …
The Power Of (Virtual) Convergence: The Unrealized Potential Of Pair Programming And Remote Work, Mikayla Maki
The Power Of (Virtual) Convergence: The Unrealized Potential Of Pair Programming And Remote Work, Mikayla Maki
University Honors Theses
Remote work is expensive. It can lead to isolation, miscommunications, and ossified organizations. These problems occur because of a synchronicity mismatch between how we need to communicate as humans, and what today's tools are capable of. This mismatch can be solved by the adoption of remote pair programming, as exemplified by the authors work at a startup (Zed). Pair programming provides the organic, synchronous, reciprocal interaction necessary to develop the sorts of relationships that remote firms currently lack.
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Systems Science Faculty Publications and Presentations
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …