Reframing the Question: Why \u3cem\u3eChevron\u3c/em\u3e - and Not a One-Size-Fits-All Interpretation of “Substantially the Same” - Should Guide a Court’s Interpretation of the Congressional Review Act’s Limitations on Future Rulemaking

The Congressional Review Act (the CRA) is a Congressional oversight tool used to overturn rules issued by federal agencies. Beyond the immediate effect of blocking an undesirable agency rule, the CRA bars an agency from issuing another rule in “substantially the same form” as the disapproved rule. But the scope of this provision’s future effect on agency rulemaking remains unclear: the statute is silent as to what criteria should be considered in evaluating whether or when a subsequent rule falls into the “substantially the same” category, and the provision has gone untested in court. Rather than proposing a uniform interpretation of “substantially the same,” this Article proposes that courts adopt a case-by-case approach to allegations that an agency is barred from enacting a particular rule due to a prior CRA resolution. Specifically, the Article argues that courts should apply Chevron and, where appropriate, defer to an agency’s conclusion that a rule is not substantially the same as a rule blocked by an earlier CRA resolution. In reaching this conclusion, the Article contends a CRA resolution effectively amends an agency’s organic statute, thereby permitting courts to apply Chevron to an agency’s determination of whether a rule does or does not fall within the CRA’s prohibitive scope

On classical string configurations

Equations which define classical configurations of strings in $R^3$ are presented in a simple form. General properties as well as particular classes of solutions of these equations are considered.Comment: 10 pages, Latex, no figures, trivial corrections, submitted to Modern Physics Letters

Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities

Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?

In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach

A Physical Limit to the Magnetic Fields of T Tauri Stars

Recent estimates of magnetic field strengths in T Tauri stars yield values $B=1$--$4\,{\rm kG}$. In this paper, I present an upper limit to the photospheric values of $B$ by computing the equipartition values for different surface gravities and effective temperatures. The values of $B$ derived from the observations exceed this limit, and I examine the possible causes for this discrepancy

Milne-Eddington inversion of the Fe I line pair at 630~nm

The iron lines at 630.15 and 630.25 nm are often used to determine the physical conditions of the solar photosphere. A common approach is to invert them simultaneously under the Milne-Eddington approximation. The same thermodynamic parameters are employed for the two lines, except for their opacities, which are assumed to have a constant ratio. We aim at investigating the validity of this assumption, since the two lines are not exactly the same. We use magnetohydrodynamic simulations of the quiet Sun to examine the behavior of the ME thermodynamic parameters and their influence on the retrieval of vector magnetic fields and flow velocities. Our analysis shows that the two lines can be coupled and inverted simultaneously using the same thermodynamic parameters and a constant opacity ratio. The inversion of two lines is significantly more accurate than single-line inversions because of the larger number of observables.Comment: Accepted for publication in Astronomy and Astrophysics (Research Note