Striving for safety, impact, and equity: A critical consideration of AJCP publications on formal youth mentoring programs.

In this virtual special issue (VSI) we curate and reflect upon 22 articles on formal youth mentoring previously published in the American Journal of Community Psychology (AJCP). First, we provide historical context and highlight AJCP's 2002 special issue on mentoring, which played an important role in establishing youth mentoring as a vibrant area of research. Next, we review and discuss findings from subsequent AJCP studies in three interrelated lines of inquiry: (1) the importance of facilitating high-quality mentoring relationships; (2) associations among youth's presenting needs, relationship quality, and outcomes; and (3) program practices leading to stronger, more impactful relationships.

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data.

We present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably.

An example of implementing a safety protocol in remote intervention and survey research with college students.

Introduction: This article draws attention to the need for open evaluation and reporting on safety protocols in survey and intervention research. We describe a protocol for responding to those who indicate increased risk of self-harm (i.e.

How the Exchange Energy Can Affect the Power Laws Used to Extrapolate the Coupled Cluster Correlation Energy to the Thermodynamic Limit.

Finite size error is commonly removed from coupled cluster theory calculations by extrapolations over correlation energy calculations of different system sizes (), where the scaling comes from the total energy rather than the correlation energy. However, previous studies in the quantum Monte Carlo community suggest an exchange-energy-like power law of should also be present in the correlation energy when using the conventional Coulomb interaction. The rationale for this is that the total energy goes as and the exchange energy goes as ; thus, the correlation energy should be a combination of these two power laws.

Machine learning for a finite size correction in periodic coupled cluster theory calculations.

We introduce a straightforward Gaussian process regression (GPR) model for the transition structure factor of metal periodic coupled cluster singles and doubles (CCSD) calculations. This is inspired by the method introduced by Liao and Grüneis for interpolating over the transition structure factor to obtain a finite size correction for CCSD [K. Liao and A.