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To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yuan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Potassium (K⁺) ion is an important biological substance in the human body and plays a critical role in the maintenance of transmembrane potential and hormone secretion. Several detection techniques, including fluorescent, electrochemical, and electrical methods, have been extensively investigated to selectively recognize K⁺ ions. In this work, a highly sensitive and selective biosensor based on single-layer graphene has been developed for K⁺ ion detection under Van der Pauw measurement configuration. With pre-immobilization of guanine-rich DNA on the graphene surface, the graphene devices exhibit a very low limit of detection (≈1 nM) with a dynamic range of 1 nM-10 μM and excellent K⁺ ion specificity against other alkali cations, such as Na⁺ ions. The origin of K⁺ ion selectivity can be attributed to the fact that the formation of guanine-quadruplexes from guanine-rich DNA has a strong affinity for capturing K⁺ ions. The graphene-based biosensors with improved sensing performance for K⁺ ion recognition can be applied to health monitoring and early disease diagnosis., Competing Interests: The authors declare no conflict of interest.