UC Irvine

Latine parents from lower socioeconomic backgrounds in the United States (US) often face challenges when supporting their adolescents’ education in subjects like math. Guided by strengths-based, culturally grounded frameworks, this study explored the challenges Latine parents faced when supporting adolescents’ math learning and how they leveraged their community cultural wealth via specific strategies to address challenges. We conducted semi-structured qualitative interviews with 20 Latine-descent parents (19 mothers, one father; 12 with less than a high school education, five with a high school education, three with some college education) of adolescents (eight girls, 12 boys; eight 6th graders, seven 7th graders, and five 8th graders) attending four middle schools in southern California. Systematic coding and theming of the interview data were used to help identify challenges parents experienced at the individual level (e.g., gaps in content/curriculum knowledge, problems with technology, linguistic differences) and at the contextual level (e.g., conflicting obligations, nonideal circumstances). Parents used their community cultural wealth by employing five strategies: (a) working closely with adolescents, (b) seeking help from their social networks, (c) providing learning spaces and organized activities to help, (d) using digital tools, and (e) hoping to build their knowledge and skills in the future. Finally, analyses revealed emergent linkages between specific math support challenges and adaptive strategies. The findings underscore the utility of leveraging parents’ cultural funds of knowledge and community cultural wealth to understand not just the math-specific needs of Latine families but also how families already actively address challenges to math support.

As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape. We created an AI-enabled writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division STEM writing-intensive course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations and instructional resources. We then share our findings from a year of design-based implementation research during the 2023–2024 academic year. Our original instruction framework identified the need for students to understand, access, prompt, corroborate, and incorporate the generative AI use effectively. In this paper, we explain the need for students to think first, before using AI, move through good enough prompting to agentic iterative prompting, and reflect on their use at the end. We also provide emerging best practices for instructors, beginning with identifying learning objectives, determining the appropriate AI role, revising the content, reflecting on the revised curriculum, and reintroducing learning as needed. We end with an indication of our future directions.

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

INTRODUCTION: In the United States, sport is a common form of youth physical activity (PA) with demonstrated health benefits. However, limited longitudinal dataexists on the psychosocial determinants and consequences of youth sport participation. This study examined grade 6 (11-12-year-old) predictors of high school organized sport participation and effects of high school sport participation on age 26 behavior, mental health and wellbeing. METHODS: Structural equation models tested relationships using the National Institute for Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD). Half of the sample was male, and played organized sports at ages 15 and 18. Eighty percent of the sample was white. RESULTS: Grade 6 predictors of playing high school sport were: child enjoys PA, parent enjoys PA, parent feels physical education (PE) is important, and vigorous PA minutes/week. Playing sports at ages 15 and 18 was associated with better wellbeing, lower depression, increased sport and fitness activity participation. Enjoyment of PA was directly associated with fitness activities at age 26, more than a decade later. High school sport participation at both age 15 and 18 further mediated relationships between enjoyment with wellbeing and depression at age 26. DISCUSSION: Sport participation is a common accessible means of PA, and participating in sports in high school is associated with better mental health and PA outcomes at age 26. Fostering enjoyment of PA during childhood helps shape PA in early adulthood and adult mental health benefits derived from high school sport participation.

Newly encoded memories are reactivated and consolidated during sleep. However, how the reactivation of a specific memory unfolds over time is poorly understood. What are the temporal dynamics of a single reactivation event within a period of sleep? Does extending a single reactivation opportunity translate to stronger memory benefits? We explored these dynamics by utilizing targeted memory reactivation (TMR), a technique that biases the consolidation of memories via the unobtrusive presentation of memory-associated cues during sleep. Participants learned the on-screen positions of sixty objects, each linked with a unique sound (e.g., cat - meow). Some sounds were then presented during non-REM sleep, with the duration allotted for reactivation causally controlled by varying the timing of the interstimulus interval. TMR did not lead to uniform improvement in memory, and no differences were observed between objects allotted short (2.5 s) and long (7.5 s) reactivation windows. However, memory for objects allotted short windows was impacted by TMR in an encoding-strength-dependent manner, with poorly encoded objects benefiting the most. Classification models trained on EEG data revealed memory reactivation that was time-locked to sound onset during sleep, and this measure of reactivation was linked with memory gains one week later. We did not find evidence for reactivation that extended beyond the time window immediately after sound onset (<2 s). Although our results are not entirely conclusive, they suggest that the critical processes supporting memory consolidation conclude within <2 s after reactivation onset and that extended reactivation windows do not confer additional benefits.

The primary strategy for addressing environmental concerns related to global aviation emissions is transitioning to low-carbon propulsion technologies. Hydrogen (H2) offers significant potential as a sustainable fuel, with anticipated zero to low carbon emissions. This study develops a methodological framework that integrates on-site electrolytic H2 production, storage, and transportation for airport applications. For the first time, the techno-economic feasibility of supplying clean liquid hydrogen (LH2) to Los Angeles International Airport (LAX) to support its transition toward sustainable operations by 2050 is comprehensively analyzed. The results underscore the critical role of integrating long-term H2 storage and short-term battery storage solutions to establish a reliable, self-sustained microgrid system at LAX. The estimated levelized cost of hydrogen (LCOH) ranges from $6.77 to $7.10 per kilogram of H2 in 2030, decreasing significantly to approximately $3.78 per kilogram of H2 by 2050, showing the viability of deploying clean H2 at LAX. Additionally, this study, for the first time, quantifies the global warming potential (GWP) of clean H2 supply pathways for airport applications, revealing a range of 0.29 to 0.35 kg CO2-eq/kg H2 by 2050, with H2 venting from electrolysis identified as the dominant contributor. The findings emphasize the feasibility of H2 as a sustainable aviation fuel and provide actionable strategies for its implementation at LAX. This work advances the hydrogen aviation field by bridging the gap between the general clean H2 supply chain strategies and the specific needs of the aviation sector, thereby contributing to California's ambitious climate goals. Future research is recommended to address limitations in cost optimization, lifecycle impacts, policy incentives, and safety innovations, enabling the scalable and practical implementation of H2 as a sustainable aviation fuel at airports.

In order to diagnose the cause of some defects in the category of canonical hypergroups, we investigate several categories of hyperstructures that generalize hypergroups. By allowing hyperoperations with possibly empty products, one obtains categories with desirable features such as completeness and cocompleteness, free functors, regularity, and closed monoidal structures. We show by counterexamples that such constructions cannot be carried out within the category of canonical hypergroups. This suggests that (commutative) unital, reversible hypermagmas—which we call mosaics—form a worthwhile generalization of (canonical) hypergroups from the categorical perspective. Notably, mosaics contain pointed simple matroids as a subcategory, and projective geometries as a full subcategory.