Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints.

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation has traditionally relied on cardiologists' expertise. Deep learning has revolutionized medical data analysis, especially within ECG diagnostics. However, the challenge of inter-patient variability limits the generalizability of ECG-AI models trained on population datasets, often reducing accuracy for specific patients or groups.

Polymer-Surface-Mediated Mechanochemical Reaction for Rapid and Scalable Manufacture of Perovskite QD Phosphors.

Perovskite quantum dots (QDs) have been considered new-generation emitters for lighting and displays due to their high photoluminescence (PL) efficiency, and pure color. However, their commercialization process is currently hindered by the challenge of mass production in a quick and environmentally friendly manner. In this study, a polymer-surface-mediated mechanochemical reaction (PMR) is proposed to prepare perovskite QDs using a high-speed multifunction grinder for the first time.

Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation.

Percutaneous coronary intervention (PCI) is a minimally invasive technique for treating vascular diseases. PCI requires precise and real-time visualization and guidance to ensure surgical safety and efficiency. Existing mainstream guiding methods rely on hemodynamic parameters.

Investigation of Fe-Ni Mixed-Oxide Catalysts for the Reduction of NO by CO: Physicochemical Properties and Catalytic Performance.

A series of Fe-Ni mixed-oxide catalysts were synthesized by using the sol-gel method for the reduction of NO by CO. These Fe-Ni mixed-oxide catalysts exhibited tremendously enhanced catalytic performance compared to monometallic catalysts that were prepared by using the same method. The effects of Fe/Ni molar ratio and calcination temperature on the catalytic activity were examined and the physicochemical properties of the catalysts were characterized by using XRD, Raman spectroscopy, N -adsorption/-desorption isotherms, temperature-programmed reduction with hydrogen (H -TPR), temperature-programmed desorption of nitric oxide (NO-TPD), and X-ray photoelectron spectroscopy (XPS).

The deterministic chaos and random noise in turbulent jet.

A turbulent flow is usually treated as a superposition of coherent structure and incoherent turbulence. In this paper, the largest Lyapunov exponent and the random noise in the near field of round jet and plane jet are estimated with our previously proposed method of chaotic time series analysis [T. L.

Estimating the largest Lyapunov exponent and noise level from chaotic time series.

A novel method for estimating simultaneously the largest Lyapunov exponent (LLE) and noise level (NL) from a noisy chaotic time series is presented in this paper. We research the influence of noise on the average distance of different pairs of points in an embedding phase space and provide a rescaled formula for calculating the LLE when the time series is contaminated with noise. Our algorithm is proposed based on this formula and the invariant of the LLE in different dimensional embedding phase spaces.