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DRIVE: A Mobile Application for Directed, Remote, Interactive Viewing and Exploring
No full textTechnologies to support remote interaction hold great potential to expand opportunities for engagement and interaction. In this work, we present a mobile application to allow remote exploration of an environment by those who cannot be physically present due to geographic or mobility limitations. Our system is designed to place control of the experience with the audience or viewer to encourage greater engagement with the remote environment. After presenting the application, we discuss how the application will be used and tested in realistic contexts and present future extensions to create a more immersive experience
Predictive modeling of low bid deviations in US transportation infrastructure projects
No full textPurpose This study aims to analyze low bid deviations in transportation infrastructure projects, identify key project and market-related factors influencing these deviations, and employ a machine learning (ML) framework to improve bid accuracy and forecasting. Design/methodology/approach This study analyzes 2,915 historical transportation project bids from Louisiana (2012–2024). The dataset was split into training (80%) and testing (20%) subsets. A two-stage feature selection process was implemented, combining Pearson correlation filtering and mutual information ranking to select the top nine predictive variables. Five ML models, LightGBM, CatBoost, XGBoost, TabNet, and a stacking ensemble, were trained and evaluated. Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) and sensitivity analysis. Findings TabNet achieved the best predictive performance, capturing complex, nonlinear relationships among variables. SHAP analysis revealed that the number of bidders, construction investment levels, and material costs were the most influential factors. Sensitivity analysis confirmed the measurable impact of construction demand and input prices on predicted deviations, while employment-related variables showed lower marginal influence. Research limitations/implications This study is limited by its reliance on historical bid data from a single U.S. state (Louisiana), which may constrain the generalizability of the findings to other regions with different procurement frameworks, contractor behaviors, and economic dynamics. Furthermore, while the selected ML models demonstrated strong predictive performance under typical market conditions, their effectiveness may decrease during periods of economic volatility or unexpected disruption. These limitations highlight the need for future research to incorporate multi-state datasets, consider temporal modeling strategies, and explore regional heterogeneity in contractor markets and procurement practices. Such expansions would help validate and enhance the robustness of ML-based frameworks in broader public infrastructure contexts. Practical implications The results underscore the practical value of using ML-based tools, such as TabNet, in transportation cost estimation and procurement planning. By identifying key predictors of bid deviations—such as competition levels, construction demand, and material prices—transportation agencies can refine early-stage cost estimates and reduce estimation bias. Additionally, using interpretable models with SHAP and sensitivity analysis empowers decision-makers with actionable insights, promoting transparency and informed procurement strategies. Encouraging broader contractor participation and integrating real-time market indicators into cost estimation workflows can ultimately enhance project planning accuracy and procurement efficiency. Originality/value This research presents a replicable, interpretable ML framework for analyzing low bid deviations in public infrastructure projects. The approach enables transportation agencies to better understand the drivers of bid variability, improve cost estimation practices, and adapt procurement strategies to evolving market conditions
Navigating Regulatory Challenges: The Impact of Labor Strikes in International Trade and the Ocean Freight Industry
No full textDoes Soft Power Work? Evaluating the Effectiveness of China’s Confucius Institutes in Latin America
No full textChina’s soft power efforts have increased, attracting global attention through diplomacy, cultural initiatives, and economic ventures. A notable example is the establishment of Confucius Institutes (CIs). This dissertation examines how China employs soft power strategies in Latin America, with a specific focus on Argentina and Chile. Drawing on a wide range of academic literature, economic data, government databases, and news sources, this study examines the role of CIs within China’s broader cultural soft power strategy to determine the effectiveness of China’s soft power efforts.
This research employs a mixed-method approach, combining a qualitative case study analysis of China’s cultural diplomacy and political, domestic, and economic trends in Argentina and Chile with a quantitative evaluation of variables such as trade balance, Foreign Direct Investment (FDI), Chinese aid, Belt and Road Initiative (BRI) participation, the establishment of CIs, and public opinion polls. A comparative analysis revealed a correlation between the establishment of CIs and China’s rise in soft power, but it did not establish causation. While imports, exports, and Chinese FDI increased before and after the establishment of CIs, public opinion polls declined, suggesting that China’s soft power efforts in Latin America are ineffective. The findings contribute to an understanding of China’s engagement strategy in Latin America and the extent to which cultural diplomacy and CIs are used as a soft power tool. Furthermore, these findings challenge the notion that Chinese soft power initiatives pose a direct threat to Western society, suggesting a shift in focus from soft power to Chinese economic strength
2024-2025: Distinguished Visiting Author, Elisa Gonzalez
No full textStudent Fellows: Benjamin Harvey, Abigail Lebowitz, Aelan Lee, May Mastrantonio, Ryan Robertsonhttps://docs.rwu.edu/bermont-fellowship/1011/thumbnail.jp
Frankenstein (Student Production) 2025 Production Photo
No full textProvidence College Department of Theatre, Dance, & Film
Bowab Studio Theatre, Smith Center for the Arts
Frankenstein by Peggy Webling, Based on the novel by Mary Wollstonecraft Shelley, Adapted and directed by Christina Schwab \u2725
February 7, 2025, 7:30pm
February 8, 2025, 2pm
Scenic Design, Lighting Design, & Properties Design, Kathryn Genest \u2725
Costume Design & Sound Design, Maisie Meehan \u2725
Faculty Advisor and Intimacy Coordinator, Erin Joy Schmidt
Vocal Coach, Megan Chang
Cast: Victor Moritz - Jude Larson; Henry Frankenstein - Owen Kruger; Dr. Waldman - Rodney Lopez; Baron Frankenstein - Gabe Joseph; Frankenstein - Kevin Bongiorno; Emilie Lavenza - Olivia Black; Baroness Frankenstein - Victoria Cannon; Katrine - Sara D\u27Andrea; Elizabeth - Madelyn Young
Photography by Peter Goldberghttps://digitalcommons.providence.edu/frankenstein_photos/1022/thumbnail.jp
Autonomic Recovery from Post-Exercise Hot Water Immersion
No full textAbigail R. Sousa ’27, Health Science major
Faculty mentor: Dr. Brett Romano Ely, Health Science