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Although different scholars have offered several reasons behind why Latinx students do not pursue STEM careers–particularly engineering–many scholars have argued that one particularly powerful reason is that the cultures of students do not fit the dominant discourse of engineering. It has been argued that curriculum materials do not portray the lived experiences and embodied knowledge of students who come from non-White, non-English-speaking backgrounds. In addition, teacher preparation has been questioned regarding the opportunities available for teachers to identify with engineering and make the curriculum more culturally relevant to students. Building this capacity is critical for the recruitment, preparation and roader participation of underserved communities in STEM. Moreover, teacher preparation is necessary to dismantle the dominant narratives in STEM and to provide the space for underrepresented students' embodied knowledge to be acknowledged, valued, and integrated into the curriculum. This project presents the ongoing efforts to analyze how a more situated view of engineering, particularly through asset-based approaches, can serve as a pathway to and through engineering for Latinx students. The goal is to provide teachers with the tools to identify, elicit, and recognize students' funds of knowledge as assets in solving engineering problems. 

The concept of funds of knowledge has been widely studied in different educational contexts. Funds of knowledge are described as the historically accumulated skills, experiences, practices, and ways of knowing that develop within a household for functioning and well-being. Sometimes these include the intellectual, communicative, emotional, resistance and even spiritual resources for learning that emerge from household and community practices. As a framework, funds of knowledge is important when trying to understand the learning processes occurring at home and communities that can be transferred into any learning environment (e.g., school, museum, library, after-school program). However, there has been little discussion on how immediate role models, such as STEM summer program facilitators, can engage in eliciting the funds of knowledge of summer enrichment program participants in order to make their experiences more enriching and culturally responsive. This pilot study sought to understand how STEM facilitators, also known as pod leaders in this study, understood “funds of knowledge” as a framework and utilized it as a tool to elicit and make the most of the funds of knowledge participants (middle school students) brought to a two-week STEM summer enrichment program. The study, which is a small piece of a much larger research endeavor, primarily relied on data collected from interviews with eight individual pod leaders. The results of this study indicated that elicitation strategies are sometimes hindered by programmatic features–primarily the time constraints and subsequent lack of time for reflection–of summer enrichment programs. 

Photometric and spectral observations of the W UMa binaries NSVS 254037 and V505 Lac with periods of around 8 hr are presented. The simultaneous light curve and radial velocity curve solutions revealed that their components are of G spectral type. The two targets undergo partial eclipses. They both have overcontact configurations of W subtype with fill‐out factorf∼ 0.1. The derived parameters of NSVS 254037 are: mass ratioq= 2.42; orbital inclinationi= 67.1°; temperaturesT₁= 5800 K andT₂= 5585 K; massesM₁= 0.47 M_⊙andM₂= 1.15 M_⊙; radiiR₁= 0.72 R_⊙andR₂= 1.07 R_⊙; luminositiesL₁= 0.53 L_⊙andL₂= 1.0 L_⊙; equatorial velocitiesV₁= 89 km⁻¹andV₂= 146 km⁻¹; and distanced= 175 pc, almost the same as those of GAIA(DR2). The derived parameters of V505 Lac are: mass ratioq= 2.53; orbital inclinationi= 57.3⁰; temperaturesT₁= 5,600 K andT₂= 5,166 K; massesM₁= 0.38 M_⊙andM₂= 0.97 M_⊙; radiiR₁= 0.69 R_⊙andR₂= 1.04 R_⊙; luminositiesL₁= 0.41 L_⊙andL₂= 0.68 L_⊙; equatorial velocitiesV₁= 106 km⁻¹andV₂= 129 km⁻¹; and distanced= 117 pc, smaller than those of GAIA by 19%. Thus, NSVS 254037 and V505 Lac turned out similar not only in periods and magnitudes but also in mass ratios, radii, and fill‐out factors.

 

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos. 

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and$7\mathrm{GeV}/c$beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be$380\pm 26\mathrm{mbarns}$for the$6\mathrm{GeV}/c$setting and$379\pm 35\mathrm{mbarns}$for the$7\mathrm{GeV}/c$setting.

Published by the American Physical Society2024 

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.