MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI.

Anodizing of iron-based alloys: fundamentals, recent progress, and applications.

This review aims to comprehensively and systematically analyze the anodic oxidation process to form nanostructured oxide films on the surface of the most technologically relevant Fe-based alloys and steels. A special emphasis is put on detailed analysis of the mechanisms of the anodic formation of Fe-based nanostructured materials. The effect of anodizing parameters including the type of Fe-alloy, electrolyte composition, potential/current regimes, as well as various post-treatment procedures (including annealing treatment) on the growth, morphology, composition, and properties of the resulting oxide films is discussed in detail.

Synthesis and Properties of Highly Tilted Antiferroelectric Liquid Crystalline () Enantiomers.

This work reports the synthesis method and various properties of four rod-like antiferroelectric () laterally substituted enantiomers, with or without fluorine atoms used as substituents in the benzene ring. The influence of fluorine substitution on the mesophase temperature range was determined. The synthesized compounds are three-ring rod-like smectics with a chiral center based on ()-(-)-2-octanol.

Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation.

Background: Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making.

Objective: This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems.