Improving the coverage area and flake size of ReSthrough machine learning in APCVD.

Machine learning is playing a crucial role in optimizing material synthesis, particularly in scenarios where several parameters related to growth exhibit different and significant outcomes. An example of such complexity is the growth of atomically thin semiconductors through chemical vapor deposition (CVD), where multiple parameters can influence the thermodynamics and reaction kinetics involved in the synthesis. Herein, we performed a set of orthogonal experiments, varying the key parameters such as temperature, carries gas flux and precursor position to identify the optimal conditions for maximizing covered area and the size of rhenium disulfide (ReS) crystals.

Modeling the Electrical Activity of the Heart via Transfer Functions and Genetic Algorithms.

Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA).

ECG Patient Simulator Based on Mathematical Models.

In this work, we propose a versatile, low-cost, and tunable electronic device to generate realistic electrocardiogram (ECG) waveforms, capable of simulating ECG of patients within a wide range of possibilities. A visual analysis of the clinical ECG register provides the cardiologist with vital physiological information to determine the patient's heart condition. Because of its clinical significance, there is a strong interest in algorithms and medical ECG measuring devices that acquire, preserve, and process ECG recordings with high fidelity.

Identification of high-risk COVID-19 patients using machine learning.

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information.

Observation of the modification of quantum statistics of plasmonic systems.

For almost two decades, researchers have observed the preservation of the quantum statistical properties of bosons in a large variety of plasmonic systems. In addition, the possibility of preserving nonclassical correlations in light-matter interactions mediated by scattering among photons and plasmons stimulated the idea of the conservation of quantum statistics in plasmonic systems. It has also been assumed that similar dynamics underlie the conservation of the quantum fluctuations that define the nature of light sources.

Cardiac Conduction Model for Generating 12 Lead ECG Signals With Realistic Heart Rate Dynamics.

We present an extended heterogeneous oscillator model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. The model consists of main natural pacemakers represented by modified van der Pol equations, and atrial and ventricular muscles, in which the depolarization and repolarization processes are described by modified FitzHugh-Nagumo equations. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the frequency-domain characteristics of heart rate variability produced by Mayer and respiratory sinus arrhythmia waves, normally distributed additive noise and a baseline wander that couple the respiratory frequency.

Noise-assisted energy transport in electrical oscillator networks with off-diagonal dynamical disorder.

Noise is generally thought as detrimental for energy transport in coupled oscillator networks. However, it has been shown that for certain coherently evolving systems, the presence of noise can enhance, somehow unexpectedly, their transport efficiency; a phenomenon called environment-assisted quantum transport (ENAQT) or dephasing-assisted transport. Here, we report on the experimental observation of such effect in a network of coupled electrical oscillators.