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Item type: Item , Mitigating Physically Implausible Energy Performance Forecasting in Residential Buildings Using Logarithmic Transformation-Based Machine Learning Analysis: A Case Study Using RECS and ResStock Datasets(ASHRAE Transactions, 2026-06) Ramnarayan, Aditya; Evren, Fatih; Gunderson, PatriciaData-driven analysis of residential building energy consumption is essential for understanding how homes perform, improving model accuracy, and guiding large-scale energy efficiency decisions. In this study, we apply several machine learning methods, including CatBoost, LightGBM, Random Forest, XGBoost, and a Neural Network, to predict total energy consumption as well as space heating and cooling loads across two diverse datasets – the Energy Information Administration’s Residential Energy Consumption Survey (RECS) of nearly 18,500 real households, and the Department of Energy’s Residential Stock (ResStock) database, a compendium of nearly 550,000 statistically-developed building simulations. To address the significant skewness often observed in energy consumption distributions, we applied a logarithmic transformation to all energy performance metrics, including total energy consumption and space heating and cooling energy use. In its raw form, residential energy performance (whether total usage, space heating, or space cooling) often exhibits a strongly right-skewed distribution: a small fraction of homes accounts for disproportionately large energy demands. Such heavy-tailed data can bias statistical models, destabilize variance estimates, and undermine predictive accuracy. By applying a natural logarithm transformation (with an additive constant, e, to handle low or zero values), we compress extremely high values and stretch values near zero, yielding a distribution that is more symmetric and statistically tractable. After fitting our models in the transformed space, we back-transform predictions to the original energy scale for interpretation. This log-transformation approach enhances the statistical robustness of our models while preserving their physical interpretability, effectively mitigating physically implausible negative forecasts and improving predictive consistency across highly skewed energy variables. These data-driven models serve as fast, scalable predictors of residential energy performance for individual homes. By basing predictions on actual (RECS) and/or modeled consumption (ResStock), they improve the accuracy of national energy demand estimates and enable identification of potential energy efficiency upgrades. This approach enhances traditional tools with greater empirical predictive accuracy, particularly for skewed end uses such as cooling. Using the logarithmic transformation approach resulted in significant model performance improvement for cooling energy consumption forecasts, with R2 values rising from 0.867 to 0.928 and decreases in MAE and RMSE values from 5943 to 4517 and 9441 to 6911, respectively, thereby underscoring the value of this approach in eliminating physically implausible values in energy performance forecasting.Item type: Item , Multi-station Neutrino Vertex Reconstruction with Convolutional Neural Networks for a Simulated RNO-G Sub-array(2026-04-22) Sued, Santiago; Clark, BrianUltra-high-energy (UHE) neutrinos serve as unique messengers for probing the most energetic environments in the Universe. The search for these elusive particles has driven the development of a new generation of in-ice detectors that utilize coherent radio Cherenkov emission (the Askaryan effect) from neutrino-induced cascades to detect neutrinos. The kilometer-scale attenuation length of radio waves in cold ice allows for the instrumentation of large detector volumes with sparse arrays. Consequently, this extended propagation range significantly increases the likelihood of coincident multi-station detections. Despite this, current reconstruction algorithms predominantly focus on reconstruction with just a single detector’s worth of information, ignoring the rich geometric and temporal interplay of multi-station events. In this work, we present a novel machine learning approach utilizing convolutional neural networks to reconstruct the neutrino interaction vertex from four-station coincident events using simulated Radio Neutrino Observatory in Greenland (RNO-G) stations. The architecture is modular, facilitating the rapid adaptation of other single-station reconstruction techniques. We demonstrate that while the model exhibits systematic under-prediction of the vertex radius, the radial precision achieves a relative 1σ (68% containment) error of 0.32, an improvement of a factor of two over the 0.60 achieved by current state-of-the-art single-station methods. These results highlight the transformative potential of deep learning techniques for exploiting multi-station coincidence in next-generation radio neutrino observatories.Item type: Item , What’s Guiding Chemical Engineering?: A LibGuide Content Analysis (Presentation)(2026 ASEE Annual Conference & Exposition, 2026) Weiss, SarahBackground LibGuides are a commonly used tool for aggregating library resources by topic. While their use is practically ubiquitous in academic libraries, the content and pedagogical approach of these guides vary widely. This is the first such published content analysis of LibGuides in the field of chemical engineering. Purpose/Hypothesis The research explores current trends in the composition of chemical engineering LibGuides including the resources most commonly presented as well as pedagogical approaches to presenting information. Method/Design/Scope While this research initially was designed with a broader scope, in its current state it presents a comprehensive analysis focusing specifically on chemical engineering LibGuides. This study considers guides from 50 of the R1 institutions listed in US News and World Report 2023-2024 Best Engineering Schools. Each guide analyzed was saved as a PDF in the summer of 2024 and uploaded to NVivo for coding of the layout, content, and pedagogy used in the guide. This subsequent data collected was then exported to Excel for clean up and analysis. Results/Conclusions The analysis results show evidence of patterns in content and pedagogical approach in chemical engineering LibGuides at R1institutions. Most notably, there is a level of consistency in the types of resources presented, particularly databases and reference works suggesting some shared disciplinary norms among engineering librarians regarding core research resources. This information is useful for those with limited knowledge in the area or those looking to create research guides or other instructional materials to support chemical engineering programs. This analysis does have limitations including being discipline limited and not addressing issues of best practice areas which could be the focus of further research.Item type: Item , A Guide to Change Management Using the SARAH Model (Poster & Supplemental Materials)(2026) Kern, Sara; Barbrow, Sarah; Jane Dooley, Sarah; E Lester, Sarah; Over, Sarah; Weiss, SarahAcademic libraries are the heart of colleges and universities and, as such, are often centrally involved in and impacted by changes in the greater higher education landscape. For those working in these spaces, familiarity with strategies for managing change is essential to support yourself and others during especially turbulent times. The SARAH model, described below in bullet points with our modifications in brackets, offers a memorable way to understand responses to change, be they our own or others. S - Shock or Surprise A – Anger [Anxiety, Alert, or Anticipation] R – Resistance or Rejection [Re-engagement, or Rationalization] A - Acceptance H - Help or Hope This is not an inevitable cycle and some people may not move through the stages in a linear manner. The existing model offers structure, but our addition adds flexibility, opportunities for positive reactions, and library-specific examples and context. This model, and our modifications to it, helps individuals identify their own or others reaction to change, recognize where they might be in the process, and respond with empathy and support. By understanding feelings and reactions of ourselves and others, we can provide thoughtful and intentional support during difficult times. United by their shared name, the Sara(h)s of ELD bring together their diverse experiences and perspectives to present the SARAH model for use in academic libraries, and particularly for those working with Engineering or STEM departments. This paper and poster will present the SARAH model alongside strategies and examples for supporting both yourself and others when managing change in a library context.Item type: Item , What’s Guiding Chemical Engineering?: A LibGuide Content Analysis(2026 ASEE Annual Conference & Exposition, 2026-06) Weiss, Sarah; DiCiesare, LeahBackground LibGuides are a commonly used tool for aggregating library resources by topic. While their use is practically ubiquitous in academic libraries, the content and pedagogical approach of these guides vary widely. This is the first such published content analysis of LibGuides in the field of chemical engineering. Purpose/Hypothesis The research explores current trends in the composition of chemical engineering LibGuides including the resources most commonly presented as well as pedagogical approaches to presenting information. Method/Design/Scope While this research initially was designed with a broader scope, in its current state it presents a comprehensive analysis focusing specifically on chemical engineering LibGuides. This study considers guides from 50 of the R1 institutions listed in US News and World Report 2023-2024 Best Engineering Schools. Each guide analyzed was saved as a PDF in the summer of 2024 and uploaded to NVivo for coding of the layout, content, and pedagogy used in the guide. This subsequent data collected was then exported to Excel for clean up and analysis. Results/Conclusions The analysis results show evidence of patterns in content and pedagogical approach in chemical engineering LibGuides at R1institutions. Most notably, there is a level of consistency in the types of resources presented, particularly databases and reference works suggesting some shared disciplinary norms among engineering librarians regarding core research resources. This information is useful for those with limited knowledge in the area or those looking to create research guides or other instructional materials to support chemical engineering programs. This analysis does have limitations including being discipline limited and not addressing issues of best practice areas which could be the focus of further research.