A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.

Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations.

A novel application of inverse gas chromatography for estimating contact angles in porous media.

Hypothesis: Surface wettability is a critical factor in multi-phase flow within porous media, a processes essential in various applications e.g. in the energy sector.

A Statistical Analysis of Fluid Interface Fluctuations: Exploring the Role of Viscosity Ratio.

Understanding multiphase flow through porous media is integral to geologic carbon storage or hydrogen storage. The current modelling framework assumes each fluid present in the subsurface flows in its own continuously connected pathway. The restriction in flow caused by the presence of another fluid is modelled using relative permeability functions.

Robot-assisted gait training in patients with various neurological diseases: A mixed methods feasibility study.

Background: Walking impairment represents a relevant symptom in patients with neurological diseases often compromising social participation. Currently, mixed methods studies on robot-assisted gait training (RAGT) in patients with rare neurological diseases are lacking. This study aimed to explore the feasibility, acceptability, goal attainment and preliminary effects of RAGT in patients with common and rare neurological diseases and understand the intervention context and process.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks.