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This paper describes the design of a hybrid coupler with a continuously variable output phase difference. This is achieved by using reconfigurable transmission lines with electrically tunable lengths controlled by two biasing voltages through varactor diodes placed across the coupler branches. The design of the coupler is based on the quadrature hybrid structure for the case where the output phase difference is 90° and on the asymmetric structure for the other cases. The proposed coupler can achieve a tunable output phase difference from 52° to 128°, while keeping a coupling coefficient of −3 dB (± 0.5 dB) over the entire desired frequency band. To validate the simulated results, a prototype working at 3.5 GHz was fabricated and tested. The measurement results show good correspondence with the simulation results, especially when the output phase difference is 90°, while a phase mismatch of less than 7° was observed for the other cases. The presented coupler would be a great asset for antenna feeding arrays, especially the Butler matrix.

This study introduces an approach to predicting cardiovascular diseases (CVDs) by leveraging neural networks and evaluates its performance against the widely recognized SCORE2 risk assessment tool. Given the significant global health impact of CVDs, our research aims to assess the effectiveness of deep learning-based models compared with traditional methods. Our methodology involves developing and deploying a neural network model capable of learning intricate patterns and relationships from a comprehensive dataset. Leveraging a large population cohort with extended follow-up enables precise and long-term risk estimation. We utilize the T-paired test to compare risk predictions between our neural network model and the SCORE2 tool. Our results indicate a notable accuracy of 0.77 for our neural network-based model in predicting CVDs, with the T-paired test revealing no significant variations in risk levels between the two methods. These findings underscore the effectiveness of neural networks as a robust tool for CVD risk prediction and advocate for further exploration and integration of these technologies to enhance cardiovascular risk assessment, thereby advancing predictive modeling in healthcare.