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    Comment on the article by Trolese T et al

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    The recent introduction of quadripolar leads for cardiac resynchronization therapy raises the question which pacing vector is most beneficial to response and which parameter gives insight into the optimal vector. The study conducted by Trolese et al. is therefore of important value and unique in its kind.1 The found correlation between maximal difference of the QRS-width (ΔQRS) and acute haemodynamic response (AHR) (ΔLV dP/dtmax) is an important finding to aid vector selection. However, their results give rise to questions

    Recent Developments in Machine Learning Techniques for Handover Optimization in 5G

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    The advent of fifth-generation (5G) technology in wireless communication systems introduced a new era of connectivity, marked by exceptional capabilities. However, the complexities introduced by heterogeneous network architectures, dynamic radio conditions, and diverse application requirements pose significant challenges to many traditional mechanisms of wireless networks, among which handover. This paper addresses these challenges by delving into the potential for machine learning techniques to optimize handover in 5G networks. We review the latest state-of-the-art machine learning methodologies, focusing on their application across various stages of the handover process. By exploring the synergy between machine learning and handover optimization, this research provides valuable insights into the novel techniques aimed at ensuring robust connectivity and enhanced quality-of-service metrics in dynamic network environments
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