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Abstract Cooling energy demand is sensitive to urban form and socioeconomic characteristics of cities. Climate change will impact how these characteristics influence cooling demand. We use random forest machine learning methods to analyze the sensitivity of cooling demand in Chicago, IL, to weather, vegetation, building type, socioeconomic, and control variables by dividing census tracts of the city into four groups: below-Q1 income–hot days; above-Q1 income–hot days; below-Q1 income–regular days; and above-Q1 income–regular days. Below-Q1 census tracts experienced an increase in cooling demand on hot days while above-Q1 census tracts did not see an increase in demand. Weather (i.e. heat index and wind speed) and control variables (i.e. month of year, holidays and weekends) unsurprisingly had the most influence on cooling demand. Among the variables of interest, vegetation was associated with reduced cooling demand for below-Q1 income on hot days and increased cooling demand for below-Q1 income on regular days. In above-Q1 income census tracts building type was the most closely associated non-weather or control variable with cooling demand. The sensitivity of cooling demand for below-Q1 income census tracts to vegetation on hot days suggests vegetation could become more important for keeping cities cool for low-income populations as global temperatures increase. This result further highlights the importance of considering environmental justice in urban design. 

Do students learn from video lessons presented by pedagogical agents of different racial and gender types equivalently to those delivered by a real human instructor? How do the race and gender of these agents impact students’ learning experiences and outcomes? In this between-subject design study, college students were randomly assigned to view a six 9-minute video lesson on chemical bonds, presented by pedagogical agents varying in gender (male, female) and race (Asian, Black, White), or to view the original lesson with a real human instructor. In comparing learning with a human instructor versus with a pedagogical agent of various races and genres, ANOVAs revealed no significant differences in learning outcomes (retention and transfer scores) or learner emotions, but students reported a stronger social connection with the human instructor over pedagogical agents. Students reported stronger positive emotions and social connections with female agents over male agents. Additionally, there was limited evidence of a race-matching effect, with White students showing greater positive emotion while learning with pedagogical agents of the same race. These findings highlight the limitations of pedagogical agents compared to human instructors in video lessons, while partially reflecting gender stereotypes and intergroup bias in instructor evaluations. 

Digital hydraulics is a discrete technology that integrates advanced dynamic system controls, digital electronics, and machine learning to enhance fluid power systems’ performance, overall efficiency, and controllability. A mechanically actuated inline three-piston variable displacement digital pump was previously proposed and designed. The inline three-piston pump incorporates complex mechanical and hydraulic subsystems and highly coupled mechanisms. The complexity of the utilized subsystems poses challenges when assessing the viability of the conceptual design. Therefore, this work focuses on designing, developing, and implementing a collaborative virtual platform involving a digitized module showcasing the internal mechanical structure of the digital pump utilizing mixed reality (MR) technology. MR technology is acknowledged as the forthcoming evolution of the human–machine interface in the real–virtual environment utilizing computers and wearables. This technology permits running simulations that examine the complexity of highly coupled systems, like the digital pump, where understanding the physical phenomenon is far too intricate. The developed MR platform permits multiple users to collaborate in a synchronized immersive MR environment to study and analyze the applicability of the pump’s design and the adequacy of the operated mechanisms. The collaborative MR platform was designed and developed on the Unity game engine, employing Microsoft Azure and Photon Unity Networking to set up the synchronized MR environment. The platform involves a fully interactive virtual module on the digital pump design, developed in multiple stages using Microsoft’s Mixed Reality Tool Kit (MRTK) for Unity and deployed in the synchronized MR environment through a HoloLens 2 MR headset. A research study involving 71 participants was carried out at Purdue University. The study’s objective was to explore the impact of the collaborative MR environment on understanding the complexity and operation of the digital pump. It also sought to assess the effectiveness of MR in facilitating collaboration among fluid power stakeholders in a synchronized digital reality setting to study, diagnose, and control their complex systems. Surveys were designed and completed by all 71 participants after experiencing the MR platform. The results indicate that approximately 75% of the participants expressed positive attitudes toward their overall MR platform experience, with particular appreciation for its immersive nature and the synchronized collaborative environment it provided. More than 70% of the participants agreed that the pump’s collaborative MR platform was essential for studying and understanding the complexity and intricacy of the digital pump’s mechanical structure. Overall, the results demonstrate that the MR platform effectively facilitates the visualization of the complex pump’s internal structure, inspection of the assembly of each of the involved subsystems, and testing the applicability of the complicated mechanisms. 

This study examined how well people can recognize and relate to animated pedagogical agents of varying ethnicities/races and genders. For both Study 1 (realistic-style agents) and Study 2 (cartoon-style agents), participants viewed brief video clips of virtual agents of varying racial/ethnic categories and gender types and then identified their race/ethnicity and gender and rated how human-like and likable the agent appeared. Participants were highly accurate in identifying Black and White agents but were less accurate for Asian, Indian, and Hispanic agents. Participants were accurate in recognizing gender differences. Participants rated all types of agents as moderately human-like, except for White agents. Likability ratings were lowest for White and male agents. The same pattern of results was obtained across two independent studies with different participants and different onscreen agents, which indicates that the results are not solely due to one specific set of agents. Consistent with the Media Equation Hypothesis and the Alliance Hypothesis, this work shows that people are sensitive to the race/ethnicity and gender of onscreen agents and relate to them differently. These findings have implications for how to design animated pedagogical agents for improved multimedia learning environments in the future and serve as a crucial first step in highlighting the possibility and feasibility of incorporating diverse onscreen virtual agents into educational computer software.