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Abstract 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. 

Thermoplastic bonded magnetic composites combine the cost-effectiveness, low mass density, and manufacturing flexibility of conventional thermoplastics with the unique characteristics of magnetic powders/ fillers to form multifunctional magneto polymeric composites that offer superior properties to conventional materials. At elevated temperatures, the magnetic properties change significantly, and the polymer matrix no longer secures the magnetic particles and can rotate freely with respect to an externally applied magnetic field. This often happens at temperatures significantly below the melting point of the polymer. To extend the thermal window of bonded magnets beyond 175 ◦C (the typical temperature of rotors in motors and generators), poly- mers such as polyetheretherketone (PEEK), polyetherimide (PEI), or other high-temperature thermoplastics have been considered suitable binders for magnetic fillers. Another suggested approach is using a surface treatment to increase the adhesion between the polymer matrix and magnetic particles. In this review, the fabrication pro- cesses to make bonded magnets by injection molding and fused filament fabrication were discussed as well as their thermal, mechanical, and magnetic performance obtained via analytical and materials characterization methods. The magnetic properties of bonded permanent magnets manufactured via different techniques were discussed in terms of the most important single magnetic parameter known as “the maximum energy product- (BH)max, which can serve as a performance index for manufacturing bonded magnets. The energy product normalized on cost or mass density are used to provide insight on the performance of bonded magnets for ap- plications driven by cost or inertia. Finally, applications of high-performance thermoplastic-based magnetic composites that can be viable for stringent engineering devices such as sensors, actuators, motors, and generators were highlighted. 

The influence of hatch spacing on tensile properties of high deposition rate gas metal arc-directed energy deposition of 316L and 316LSi was explored 

The Computer Science Teachers Association (CSTA) K-12 Computer Science Standards identify ‘Algorithms and Programming’ as a key CS concept across all grade bands that encompasses sub-concepts such as algorithms, decomposition, variables, and control structures. Previous studies have shown that algorithms and programming concepts often pose challenges for novice programmers, and that instruction in these areas is often superficial. We developed formative assessment tasks to investigate middle school students’ understanding of four CS standards related to algorithms and programming and collected responses from over 100 students associated with five different teachers. We found that students demonstrated a limited understanding of the standards. These findings contribute to the growing literature on middle school students’ understanding of algorithms and programming, and provide insights that can inform CS teacher development, instruction, and curriculum design. 

The "Computer Science for All" initiative advocates for universal access to computer science (CS) instruction. A key strategy toward this end has been to establish CS content standards outlining what all students should have the opportunity to learn. Standards can support curriculum quality and access to quality CS instruction, but only if they are used to inform curriculum design and instructional practice. Professional learning offered to teachers of CS has typically focused on learning to implement a specific curriculum, rather than deepening understanding of CS concepts. We set out to develop a set of educative resources, formative assessment tools and teacher professional development (PD) sessions to support middle school CS teachers' knowledge of CS standards and standards-aligned formative assessment literacy. Our PD and associated resources focus on five CS standards in the Algorithm and Programming strand and are meant to support teachers using any CS curriculum or programming language. In this experience report, we share what we learned from implementing our standards-based PD with four middle school CS teachers. Teachers initially perceived standards as irrelevant to their teaching but they came to appreciate how a deeper understanding of CS concepts could enhance their instructional practice. Analysis of PD observations and exit surveys, teacher interviews, and teacher responses to a survey assessing CS pedagogical content knowledge demonstrated the complexity of using content standards as a driver of high-quality CS instruction at the middle school level, and reinforced our position that more standards-focused PD is needed. 

Climate in the Arctic is changing at a rapid pace. When vegetation reacts to these changes, chemicals called biogenic volatile organic compounds (BVOCs) can be released into the atmosphere in new ways. This project seeks to investigate how climate change affects the quantity and type of BVOCs released into the atmosphere on the North Slope of Alaska (NSA). In addition, we are interested in the chemical reactions these BVOCs undergo in the Arctic atmosphere. Project goals will be accomplished through field work on the NSA, and collection and laboratory analysis of atmospheric samples. Specifically, the project intends to measure the concentration of BVOCs and their secondary organic aerosol products during North Slope of Alaska field campaigns. In addition to BVOCs and organic acids, the measurements include additional baseline measurements of other volatile organic compounds (VOC) and aerosol components. We are reporting inorganic ions, alkanes, and polycyclic aromatic hydrocarbons (PAHs) for aerosol composition and select aromatic and oxidized VOCs. The time period for these detailed measurements is Jun - Aug 2023 for Utqiagvik, Alaska (AK). VOC measurements were made by proton transfer reaction mass spectrometry. The proton transfer reaction mass spectrometer (PTR-MS) was operated with Hydronium (H3O+) ion at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) site in Utqiagvik, AK from 170623 to 130823. Total suspended particulate matter samples were collected on quartz fiber filters at a roughly weekly schedule. These filters were then used for offline analysis. Offline measurement of cations and anions was conducted using ion chromatography. Offline measurement of alkanes and PAH was conducted using thermal desorption gas chromatography - mass spectrometry. 

Abstract BackgroundHybridization between evolutionary lineages has profound impacts on the fitness and ecology of hybrid progeny. In extreme cases, the effects of hybridization can transcend ecological timescales by introducing trait novelty upon which evolution can act. Indeed, hybridization can even have macroevolutionary consequences, for example, as a driver of adaptive radiations and evolutionary innovations. Accordingly, hybridization is now recognized as a motor for macrobial evolution. By contrast, there has been substantially less progress made towards understanding the positive eco-evolutionary consequences of hybridization on holobionts. Rather, the emerging paradigm in holobiont literature is that hybridization disrupts symbiosis between a host lineage and its microbiome, leaving hybrids at a fitness deficit. These conclusions, however, have been drawn based on results from predominantly low-fitness hybrid organisms. Studying “dead-end” hybrids all but guarantees finding that hybridization is detrimental. This is the pitfall that Dobzhansky fell into over 80 years ago when he used hybrid sterility and inviability to conclude that hybridization hinders evolution. Goldschmidt, however, argued that rare saltational successes—so-called hopeful monsters—disproportionately drive positive evolutionary outcomes. Goldschmidt’s view is now becoming a widely accepted explanation for the prevalence of historical hybridization in extant macrobial lineages. Aligning holobiont research with this broader evolutionary perspective requires recognizing the importance of similar patterns in host–microbiome systems. That is, rare and successful “hopeful holobionts” (i.e., hopeful monsters at the holobiont scale) might be disproportionately responsible for holobiont evolution. If true, then it is these successful systems that we should be studying to assess impacts of hybridization on the macroevolutionary trajectories of host–microbiome symbioses. ResultsIn this paper, we explore the effects of hybridization on the gut (cloacal) and skin microbiota in an ecologically successful hybrid lizard,Aspidoscelis neomexicanus. Specifically, we test the hypothesis that hybrid lizards have host-associated (HA) microbiota traits strongly differentiated from their progenitor species. Across numerous hybrid microbiota phenotypes, we find widespread evidence of transgressive segregation. Further, microbiota restructuring broadly correlates with niche restructuring during hybridization. This suggests a relationship between HA microbiota traits and ecological success. ConclusionTransgressive segregation of HA microbiota traits is not only limited to hybrids at a fitness deficit but also occurs in ecologically successful hybrids. This suggests that hybridization may be a mechanism for generating novel and potentially beneficial holobiont phenotypes. Supporting such a conclusion, the correlations that we find between hybrid microbiota and the hybrid niche indicate that hybridization might change host microbiota in ways that promote a shift or an expansion in host niche space. If true, hybrid microbiota restructuring may underly ecological release from progenitors. This, in turn, could drive evolutionary diversification. Using our system as an example, we elaborate on the evolutionary implications of host hybridization within the context of holobiont theory and then outline the next steps for understanding the role of hybridization in holobiont research.