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Many college students have neither the interest in nor the academic preparation for pursuing the study of science, technology, engineering, and mathematics (STEM). This is especially true for computer science (Code.org, 2021). Consequently, the U.S. likely faces a mismatch between its projected technology labor force needs and the majors of future college graduates (Justice et al., 2022; U.S. Bureau of Labor Statistics, 2023). A further complication of this mismatch is that the majority of U.S. students who study technology are males from middle-class White or Asian Rim ethnic (Fry et al., 2021). Lower-income youth from all race/ethnic backgrounds, females, and students from Black, Latinx, some Asian American Pacific Islander (AAPI), and Native American ethnic groups are underrepresented in computer science (CS) relative to their proportion in the overall U.S. population (U.S. Census Bureau, 2018). This disproportionality leaves large segments of the U.S. population outside the professional computing community. Addressing the second issue could help alleviate the first problem by bringing currently marginalized segments of the population into technology fields. This article presents a descriptive case study of a three-year middle school intervention designed to improve the likelihood that more low-income, female, and racial/ethnic minoritized middle school youth will be inspired and prepared to study technology in high school enroute to pursuing technology majors in college. It describes the development, implementation, and outcomes of a partnership between the University of North Carolina at Charlotte (UNC Charlotte) and Wilson STEM Academy, a public middle school serving the underrepresented populations previously described (Mickelson, 2015). 

ABSTRACT Microbial nitrogen fixation (diazotrophy) is a critical ecological process. We curated DiazoTIME (Diazotroph Taxonomic Identity and MEtabolism), a comprehensive database of diazotroph genomes including taxonomic annotation and metabolic prediction. DiazoTIME is unique among databases for classifying diazotrophs because it resolves both taxonomy and metabolic functionality. 

A smart home with a controller that can understandand predict the interaction between the external environment and the user’s behavior and preferences can provide significant energy efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model’s temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers. 

Abstract Peat mosses ( Sphagnum spp.) are keystone species in boreal peatlands, where they dominate net primary productivity and facilitate the accumulation of carbon in thick peat deposits. Sphagnum mosses harbor a diverse assemblage of microbial partners, including N 2 ‐fixing (diazotrophic) and CH 4 ‐oxidizing (methanotrophic) taxa that support ecosystem function by regulating transformations of carbon and nitrogen. Here, we investigate the response of the Sphagnum phytobiome (plant + constituent microbiome + environment) to a gradient of experimental warming (+0°C to +9°C) and elevated CO 2 (+500 ppm) in an ombrotrophic peatland in northern Minnesota (USA). By tracking changes in carbon (CH 4 , CO 2 ) and nitrogen (NH 4 ‐N) cycling from the belowground environment up to Sphagnum and its associated microbiome, we identified a series of cascading impacts to the Sphagnum phytobiome triggered by warming and elevated CO 2 . Under ambient CO 2 , warming increased plant‐available NH 4 ‐N in surface peat, excess N accumulated in Sphagnum tissue, and N 2 fixation activity decreased. Elevated CO 2 offset the effects of warming, disrupting the accumulation of N in peat and Sphagnum tissue. Methane concentrations in porewater increased with warming irrespective of CO 2 treatment, resulting in a ~10× rise in methanotrophic activity within Sphagnum from the +9°C enclosures. Warming's divergent impacts on diazotrophy and methanotrophy caused these processes to become decoupled at warmer temperatures, as evidenced by declining rates of methane‐induced N 2 fixation and significant losses of keystone microbial taxa. In addition to changes in the Sphagnum microbiome, we observed ~94% mortality of Sphagnum between the +0°C and +9°C treatments, possibly due to the interactive effects of warming on N‐availability and competition from vascular plant species. Collectively, these results highlight the vulnerability of the Sphagnum phytobiome to rising temperatures and atmospheric CO 2 concentrations, with significant implications for carbon and nitrogen cycling in boreal peatlands.