Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.

Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature.

Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation.

The Adaptiveness of Emotion Regulation Variability and Interoceptive Attention in Daily Life.

Objective: In daily life, we must dynamically and flexibly deploy strategies to regulate our emotions, which depends on awareness of emotions and internal bodily signals. Variability in emotion-regulation strategy use may predict fewer negative emotions, especially when people pay more attention to their bodily states-or have greater "interoceptive attention" (IA). Using experience sampling, this study aimed to test whether IA predicts variability in strategy use and whether this variability and IA together predict negative affect.

Reciprocal effects between negative affect and emotion regulation in daily life.

The extended process model of emotion regulation provides a framework for understanding how emotional experiences and emotion regulation (ER) mutually influence each other over time. To investigate this reciprocal relationship, 202 adults completed a ten-day experience-sampling survey capturing levels of negative affect (NA) experience and use of ten ER strategies in daily life. Residual dynamic structural equation models (DSEMs) were used to examine within-person cross-lagged and autoregressive effects of NA and ER (strategy use and between-strategy variability).

Impact of random dopant fluctuation effect on surrounding gate MOSFETs: from atomic level simulation to circuit performance evaluation.

This paper investigates the impact of random dopant fluctuation effect on surrounding gate MOSFET, from atomic statistical simulation of device to circuit performance evaluation. The doping profile is generated by an analysis of each lattice atom and then the threshold voltage variation is obtained by device Drift-Diffusion simulation. Then the circuit performance evaluation is performed by feeding the result into a surrounding-gate MOSFET model.