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Unstructured construction accident narratives often contain latent behavioral signals, such as fatigue, overconfidence, or rushing. These allow an enhanced understanding of accident context but are rarely captured through traditional extraction methods. This paper presents a modular ReAct (Reasoning + Acting) agent framework designed to infer contextually relevant behavioral contributor in each narrative. The ReAct paradigm enables the agent to engage in step-by-step reasoning, invoke specialized tools, and iteratively refine its understanding through Thought–Action–Observation loops. This structure addresses key limitations of static Large Language Model (LLM) outputs by introducing transparent, modular, and adaptable reasoning. Our contribution lies in operationalizing this framework for behavioral insight generation. We achive this by developing and applying a behavior prioritization module to a real-world dataset of construction accident reports. This modules enables the agent to produce structured outputs including selected behaviors, confidence levels, and justifications, which in turn facilitates a behavior-aware interpretation of construction accidents. This is accomplished without requiring supervision or fine-tuning of Foundation Models. This work highlights how ReAct-style agents can deepen narrative understanding and offer a foundation for explainable behavioral analytics in safety-critical domains. 

Multirotor drones (part of the category of small Uncrewed Aerial Systems [sUAS] or small Uncrewed Aerial Vehicles [sUAV]) are used in atmospheric research to make measurements of the lower atmosphere, and their use is poised to increase in the future. New drone atmospheric sensing opportunities, such as ride-along applications and drone swarms, are emerging. These opportunities, which may not allow room for specialized shielding or aspiration equipment, together with increased drone usage, necessitate the characterization of the performance of unshielded sensors mounted to drones if the accuracy of such observations is to be understood. In this work, we characterize the accuracy of thermodynamic measurements, specifically temperature and water vapor mixing ratio, based on the sensor mounting position onboard multirotor drones. To assess the influence of the drone mechanics on the measurements, ninety-eight individual drone flights with eight distinct thermodynamic sensor positions were performed next to an instrumented flux tower and a tethersonde carrying identical sensors, where the tower and tethersonde measurements are assumed as truth. The flights were at least nine minutes in length, and nine of the flights were conducted at night. At the best position, absolute daytime temperature errors were between -0.83K and +0.61K at the 95% confidence interval, while nighttime temperature errors were smaller, ranging from -0.28 K and +0.48 K. Water vapor mixing ratio errors are within -0.22 g kg-1 and +0.66 g kg-1. We conclude that measurements in field campaigns are more accurate when sensors are placed away from the main body of the drone and are sufficiently aspirated, such as a position near, but not directly under, a spinning propeller. 

Abstract. Multirotor drones (part of the category of small Uncrewed Aerial Systems [sUAS] or small Uncrewed Aerial Vehicles [sUAV]) are used in atmospheric research to make measurements of the lower atmosphere, and their use is poised to increase in the future. New drone atmospheric sensing opportunities, such as ride-along applications and drone swarms, are emerging. These opportunities, which may not allow room for specialized shielding or aspiration equipment, together with increased drone usage, necessitate the characterization of the performance of unshielded sensors mounted to drones if the accuracy of such observations is to be understood. In this work, we characterize the accuracy of thermodynamic measurements, specifically temperature and water vapor mixing ratio, based on the sensor mounting position onboard multirotor drones. To assess the influence of the drone mechanics on the measurements, ninety-eight individual drone flights with eight distinct thermodynamic sensor positions were performed next to an instrumented flux tower and a tethersonde carrying identical sensors, where the tower and tethersonde measurements are assumed as truth. The flights were at least nine minutes in length, and nine of the flights were conducted at night. At the best position, absolute daytime temperature errors were between −0.83 and +0.61 K at the 95 % confidence interval, while nighttime temperature errors were smaller, ranging from −0.28 and +0.48 K. Water vapor mixing ratio errors are within −0.22 and +0.66 g kg−1. We conclude that measurements in field campaigns are more accurate when sensors are placed away from the main body of the drone and are sufficiently aspirated, such as a position near, but not directly under, a spinning propeller. 

Graduate student instructors (GSIs) in mathematics play a pivotal role in shaping undergraduate education and are the future of collegiate mathematics faculty. As part of their development, GSIs are expected to engage in teaching-focused professional development (TPD), particularly in evidence-based strategies like Active Learning (AL) methods. However, higher education is only beginning to explore how to effectively measure GSIs' growth in teaching skills through such TPD. This study examines the learning process of 47 novice GSIs from three universities, specifically focusing on their evolving understanding of AL before and after participating in TPD. By analyzing the GSIs' own definitions of AL, the research highlights changes in their knowledge and alignment with the intended TPD outcomes. The findings provide insight into the effectiveness of TPD on AL, while also offering recommendations for structuring future evaluations of TPD impact on GSI teaching knowledge and skills. 

Feasible and developmentally appropriate sociotechnical approaches for protecting youth from online risks have become a paramount concern among human-computer interaction research communities. Therefore, we conducted 38 interviews with entrepreneurs, IT professionals, clinicians, educators, and researchers who currently work in the space of youth online safety to understand the different sociotechnical approaches they proposed to keep youth safe online, while overcoming key challenges associated with these approaches. We identified three approaches taken among these stakeholders, which included 1) leveraging artificial intelligence (AI)/machine learning to detect risks, 2) building security/safety tools, and 3) developing new forms of parental control software. The trade-offs between privacy and protection, as well as other tensions among different stakeholders (e.g., tensions toward the big-tech companies) arose as major challenges, followed by the subjective nature of risk, lack of necessary but proprietary data, and costs to develop these technical solutions. To overcome the challenges, solutions such as building centralized and multi-disciplinary collaborations, creating sustainable business plans, prioritizing human-centered approaches, and leveraging state-of-art AI were suggested. Our contribution to the body of literature is providing evidence-based implications for the design of sociotechnical solutions to keep youth safe online. 

Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster.