Investigation of Deepfake Voice Detection Using Speech Pause Patterns: Algorithm Development and Validation.

Background: The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of "deepfake" technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information.

Objective: To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices.

An In-Silico model for evaluating the directional shock vectors in terminating and modulating rotors.

Out-of-hospital cardiac arrest (OHCA) accounts for a majority of mortality worldwide. Survivability from an OHCA highly depends on timely and effective defibrillation. Most of the OHCA cases are due to ventricular fibrillation (VF), a lethal form of cardiac arrhythmia.

Effect of Shock Vector Orientation in Modulating and Terminating Rotors - a Simulation Study.

The main treatment option for Ventricular Fibrillation (VF), especially in out-of-hospital cardiac arrests (OHCA) is defibrillation. Typically, the survival-to-discharge rates are very poor for OHCA. Existing studies have shown that rotors may be the sources of arrhythmia and ablating them could modulate or terminate VF.