HELIN Digital Commons
Not a member yet
62477 research outputs found
Sort by
Fall Dances: \u3ci\u3eMemory\u3c/i\u3e 2025 Concert Photo
No full textProvidence College Department of Theatre, Dance & Film
Fall Dances: Memory 2025
November 21, 2025, 7:30pm
November 22, 2025, 2:00pm
Choreographers: Michaela Pendola, Heather Ahern, Nina Kossler, Debra Meunier, Katrina Pavao, Colin Stilwell, Rowan Locascio-McLaughlin, Emma Daigneau, Elizabeth Graney, Grace Sittig, Alyssa Rago, Kiley Vallee
Dancers: Lola Blessington, Emma Daigneau, Madyson Daley, Noël Rose DeRosa, Ella Ford, Caroline Guanci, Amanda Kleinman, Emma Preston, Katherine Pritchard, Alyssa Rago, Kiley Vallee, Lauren Gerew, Penelope Kearns, Heidi Nicholls, Elizabeth Graney, Anna Bernat, Kendal Forslund, Helena Gillin, Gabriella Girard, Anna Gross, Sophia Quinn, Jade Bocciarelli, Simone Chabot, Caroline Puccini, Grace Sittig, Heather Ahern, Elaina Bartolini, Taylor Gifford
Photography by Mark Turekhttps://digitalcommons.providence.edu/fdc_2025_memory_photos/1015/thumbnail.jp
The Cowl - v. 89 n. 6 - October 30, 2025
No full textThe Cowl - student newspaper of Providence College. Vol. 89 No. 6 - October 30, 2025. 20 pages
The Cowl - v. 89 n. 9 - November 20, 2025
No full textThe Cowl - student newspaper of Providence College. Vol. 89 No. 9 - November 20, 2025. 24 pages
Digital Twin-Enabled Framework for Intelligent Monitoring and Anomaly Detection in Multi-Zone Building Systems
No full textThe growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. Despite growing interest, key challenges remain, including the neglect of short- and long-term forecasting across different scenarios, insufficiently robust data preparation, and the rare validation of models on multi-zone buildings over extended test periods. To address these gaps, this study presents a comprehensive DT-enabled framework for predictive monitoring and anomaly detection, validated in a multi-zone educational building in Rhode Island, USA, using a full year of operational data for validation. The proposed framework integrates a robust data processing pipeline and a comparative analysis of machine learning models, including LSTM, RNN, GRU, ANN, XGBoost, and RF, to forecast short-term (1 h) and long-term (24 h) indoor temperature variations. The LSTM model consistently outperformed other methods, achieving R2 \u3e 0.98 and RMSE \u3c 0.55 °C for all tested rooms. For real-time anomaly detection, we applied the hybrid LSTM–Interquartile Range (IQR) method on one-step-ahead residuals, which successfully identified anomalous deviations from expected patterns. The model’s predictions remained within a ±1 °C error margin for over 90% of the test data, providing reliable forecasting up to 16 h ahead. This study contributes a validated, generalizable DT methodology that addresses key research gaps, offering practical tools for predictive maintenance and operational optimization in complex building environments
Reframing Infrastructure as Civic Space: Lessons from Five Architectural Design Studios
No full textThis paper presents five architectural studios that reframe infrastructure as a civic space, engaging students in critical design research that challenges professional boundaries. Civic projects begin by identifying societal needs, which are often ill-defined and involve contested interests, demanding navigation through conflict, resistance, and uncertainty. Each studio interrogates systems typically excluded from architectural discourse, and through research, resistance, and design, students cultivate civic imagination and devise spatial strategies for sites that both sustain or divide communities, advancing architecture’s role beyond shaping of physical form
