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Leveraging Innovation and Optimizing Nurturing in STEM (NSF S-STEM #2130022, known locally as LION STEM Scholars) is a program developed to serve low-income undergraduate Engineering students at Penn State Berks, a regional campus of the Pennsylvania State University. As part of the program, scholars participate in a four-year comprehensive multi- tiered mentoring program and cohort experience. The LION STEM curricular program includes Engineering Ahead (a 4-week summer residential math-intensive bridge program prior to entering college), a first semester First-Year Seminar, and a second semester STEM-Persistence Seminar. Co-curricular activities focus on professional communication skills, financial literacy, career readiness, undergraduate research, and community engagement. The program seeks to accomplish four goals: (1) adapt, implement, and analyze evidence-based curricular and co- curricular activities to support, retain, and graduate a diverse set of the project's engineering scholars, (2) implement, test, and study through research and project evaluation strategies for systematically supporting student academic and career pathways in STEM, including development of STEM identity, (3) contribute to the knowledge base through investigation of the project's four-year multi-modal program so that other colleges may successfully implement similar programs, and (4) disseminate outcomes and findings related to the supports and interventions that promote student success to other institutions working to support low-income STEM students. The purpose of this paper is to analyze data from a repeated-measures design to provide a holistic narrative about the effects that the academic and support activities offered to LION STEM Scholars have on the development of their future-engineer role identity throughout their first year as an undergraduate engineering student. This paper presents data collected from semi- structured (Smith & Osborn, 2007) audio-recorded interviews from the first cohort of LION STEM Scholars (n=7) at three different time points (pre-summer bridge, post-summer bridge, end of first semester) as well as data collected from a written survey at the end of scholars’ second semester. 

Plasma-based acceleration has emerged as a promising candidate as an accelerator technology for a future linear collider or a next-generation light source. We consider the plasma wakefield accelerator (PWFA) concept where a plasma wave wake is excited by a particle beam and a trailing beam surfs on the wake. For a linear collider, the energy transfer efficiency from the drive beam to the wake and from the wake to the trailing beam must be large, while the emittance and energy spread of the trailing bunch must be preserved. One way to simultaneously achieve this when accelerating electrons is to use longitudinally shaped bunches and nonlinear wakes. In the linear regime, there is an analytical formalism to obtain the optimal shapes. In the nonlinear regime, however, the optimal shape of the driver to maximize the energy transfer efficiency cannot be precisely obtained because currently no theory describes the wake structure and excitation process for all degrees of nonlinearity. In addition, the ion channel radius is not well defined at the front of the wake where the plasma electrons are not fully blown out by the drive beam. We present results using a novel optimization method to effectively determine a current profile for the drive and trailing beam in PWFA that provides low energy spread, low emittance, and high acceleration efficiency. We parameterize the longitudinal beam current profile as a piecewise-linear function and define optimization objectives. For the trailing beam, the algorithm converges quickly to a nearly inverse trapezoidal trailing beam current profile similar to that predicted by the ultrarelativistic limit of the nonlinear wakefield theory. For the drive beam, the beam profile found by the optimization in the nonlinear regime that maximizes the transformer ratio also resembles that predicted by linear theory. The current profiles found from the optimization method provide higher transformer ratios compared with the linear ramp predicted by the relativistic limit of the nonlinear theory. 

—We describe how classical supercomputing can aid unreliable quantum processors of intermediate size to solve large problem instances reliably. We advocate using a hybrid quantum-classical architecture where larger quantum circuits are broken into smaller sub-circuits that are evaluated separately, either using a quantum processor or a quantum simulator running on a classical supercomputer. Circuit compilation techniques that determine which qubits are simulated classically will greatly impact the system performance as well as provide a tradeoff between circuit reliability and runtime. 

Secondary production, the growth of new heterotrophic biomass, is a key process in aquatic and terrestrial ecosystems that has been carefully measured in many flowing water ecosystems. We combine structural equation modeling with the first worldwide dataset on annual secondary production of stream invertebrate communities to reveal core pathways linking air temperature and precipitation to secondary production. In the United States, where the most extensive set of secondary production estimates and covariate data were available, we show that precipitation-mediated, low–stream flow events have a strong negative effect on secondary production. At larger scales (United States, Europe, Central America, and Pacific), we demonstrate the significance of a positive two-step pathway from air to water temperature to increasing secondary production. Our results provide insights into the potential effects of climate change on secondary production and demonstrate a modeling framework that can be applied across ecosystems.