Large Language Models for Mental Health Applications: Systematic Review.

Background: Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate.

Objective: This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on their applicability and efficacy in early screening, digital interventions, and clinical settings.

Choice architecture promotes sustainable choices in online food-delivery apps.

Greenhouse gas emissions from the food system constitute about one-third of the global total, hence mitigation in this sphere of human activity is a vital goal for research and policy. This study empirically tests the effectiveness of different interventions to reduce the carbon footprint of food choices made on food-delivery apps, using an incentive-compatible online randomized controlled trial with 4,008 participants. The experiment utilized an interactive web platform that mimics popular online food-delivery platforms (such as Just Eat) and included three treatment conditions: a sign-posted meat tax, a carbon-footprint label, and a choice-architecture intervention that changed the order of the menu so that the lowest carbon-impact restaurants and dishes were presented first.

Managing the heat: In-Vessel Components.

The Spherical Tokamak for Energy Production (STEP) programme aims to deliver a first-of-a-kind fusion prototype powerplant (SPP). The SPP plasma places extreme heat, particle and structural loads onto the plasma-facing components (PFCs) of the divertor, limiters and inboard and outboard sections of the first wall. The PFCs must manage the heat and particle loads and wider powerplant requirements relating to safety, net power generation, tritium breeding and plant availability.