Fracton Self-Statistics.

Fracton order describes novel quantum phases of matter that host quasiparticles with restricted mobility and, thus, lies beyond the existing paradigm of topological order. In particular, excitations that cannot move without creating multiple excitations are called fractons. Here, we address a fundamental open question-can the notion of self-exchange statistics be naturally defined for fractons, given their complete immobility as isolated excitations? Surprisingly, we demonstrate how fractons can be exchanged and show that their self-statistics is a key part of the characterization of fracton orders.

Operationalizing and Testing the Concept of a Physical Activity Desert.

Background: The prevalence of childhood obesity is higher in economically and socially deprived areas. Higher levels of physical activity reduce the risk of excessive weight gain in youth, and research has focused on environmental factors associated with children's physical activity, though the term "physical activity desert" has not come into wide use.

Methods: This exploratory study operationalized the term "physical activity desert" and tested the hypothesis that children living in physical activity deserts would be less physically active than children who do not.

(Thio)urea-Based Covalent Organic Framework as a Hydrogen-Bond-Donating Catalyst.

Two-dimensional urea- and thiourea-containing covalent organic frameworks (COFs) were synthesized at ambient conditions at large scale within 1 h in the absence of an acid catalyst. The site-isolated urea and thiourea in the COF showed enhanced catalytic efficiency as a hydrogen-bond-donating organocatalyst compared to the molecular counterparts in epoxide ring-opening reaction, aldehyde acetalization, and Friedel-Crafts reaction. The COF catalysts also had excellent recyclability.

Prediction of p Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines.

The acid-base dissociation constant, p, is a key parameter to define the ionization state of a compound and directly affects its biopharmaceutical profile. In this study, we developed a novel approach for p prediction using rooted topological torsion fingerprints in combination with five machine learning (ML) methods: random forest, partial least squares, extreme gradient boosting, lasso regression, and support vector regression. With a large and diverse set of 14 499 experimental p values, p models were developed for aliphatic amines.