The problem of estimating non-resonant astrophysical S-factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied in the past to this problem, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extra-solar planets, gravitational waves, and type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S-factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the d(p,γ) 3 He, 3 He( 3 He,2p) 4 He, and 3 He(α,γ) 7 Be reactions, important for deuterium burning, solar neutrinos, and big bang nucleosynthesis.

PurposeThe novel coronavirus, COVID-19, which emerged in 2019 and quickly spread to the United States, resulted in widespread closure of PreK-12 schools and universities and a rapid transition to online learning. There are concerns about how students in high-needs school districts will engage with online learning, given the limited access many disadvantaged students have to Internet and computers. Accordingly, the purpose of this study is to determine teacher perceptions of students' access and participation to online learning, as well as concerns about educational outcomes among different groups of learners.Design/methodology/approachThe authors surveyed 300 K-12 teachers in NY state about the tools and accommodations they employed in their online teaching, whether their students were participating in the online learning and the reasons for their lack of participation.FindingsRespondents reported that nearly 30% of all of their students were not regularly completing their assignments. Students in high-needs districts were significantly more likely to not complete their work. Teachers reported being very concerned about their students' educational outcomes, particularly students with disabilities (SWDs) and English language learners (ELLs). Respondents also provided suggestions for improving educational access to online learning in the future.Originality/valueNo published research has yet examined student compliance in online learning during an emergency and, in particular, during this unprecedented time of the COVID-19 pandemic and months-long stay-at-home orders.

In the PV Fleet Performance Data Initiative, high‐frequency data from commercial and utility‐scale photovoltaic (PV) systems have been collected to examine performance loss rates (PLRs) at a fleet scale. To date, performance data from more than 7.2‐gigawatt (GW) capacity, 1700 sites and 19,000 inverters—approximately equivalent to 6% to 7% of the entire US PV market—have been collected. An overall PLR of −0.75%/year was found, which is in line with historical and recent findings. Tracked silicon (Si) and cadmium telluride (CdTe) performed comparably with all fixed‐tilt systems. Higher PLRs were found for hotter temperature zones; cooler climates exhibit a median −0.48%/year loss, which increases to −0.88%/year in hotter climates. High‐efficiency module technologies showed median PLRs in line with conventional Si technologies but demonstrated markedly different PLR behavior when filtered only for low‐light conditions <600 W/m2. Causes for this technology‐dependent behavior are under investigation.

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