Child mental health and income gradient from early childhood to adolescence: Evidence from the UK.

Background: Children from low income families are likely to have poorer mental health than their more affluent peers. However, it is unclear how this association varies at different developmental stages and what the potential underpinning mechanisms are. This study investigates the relationship between family income and mental health problems from early childhood to adolescence in the UK, and examines the potential mediating role of family-related factors over time.

Simple Fabrication of Polyvinylidene Fluoride/Graphene Composite Membrane with Good Lipophilicity for Oil Treatment.

Graphene (GE) is an emerging type of two-dimensional functional nanoparticle with a tunable passageway for oil molecules. Herein, polyvinylidene fluoride (PVDF)/GE composite membranes with controllable pore structure were fabricated with a simple non-solvent-induced phase separation method. The change of crystallinity and crystal structure (α, β, γ, etc.

Eliciting risk preferences that predict risky health behavior: A comparison of two approaches.

Information on attitudes to risk could increase understanding of and explain risky health behaviors. We investigate two approaches to eliciting risk preferences in the health domain, a novel "indirect" lottery elicitation approach with health states as outcomes and a "direct" approach where respondents are asked directly about their willingness to take risks. We compare the ability of the two approaches to predict health-related risky behaviors in a general adult population.

A Weighting Method for Feature Dimension by Semisupervised Learning With Entropy.

In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large.

A Novel Neural Model With Lateral Interaction for Learning Tasks.

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity.