Abstract
Subject-independent seizure detection algorithms are typically grounded in scalp electroencephalogram (EEG) databases, due to standardized channels and locations of EEG electrodes. Intracranial EEG (iEEG) has the characteristics of low noise and high temporal resolution compared with scalp EEG. However, it is still a big challenge for seizure detection using iEEG, because of the inconsistent number and locations of implanted electrodes in different patients, which results in a lack of unified algorithms. This study introduces an innovative approach for subject-independent seizure detection using iEEG, combining channel-wise mixup, transformer networks, and multi-task learning. Channel-wise mixup enhances data utilization by effectively leveraging information from different subjects, while multi-task learning improves the generalization of the model by concurrently optimizing both the seizure detection and the subject recognition tasks. 2983 files from two well-known epilepsy databases, i.e. SWEC-ETHZ and HUP were used in our study and the result showed that our approach surpasses currently existing methods. In terms of accuracy and generalization of seizure detection, our method achieved an area under the receiver operating characteristic curve (AUC) of 0.97 and 0.95 on the two databases respectively, which are significantly higher than the result of the currently existing methods. This study proposed anew method with great potential for surgery planning of epilepsy patients.

Abstract

Abstract
This research addresses the Frequency Setting Problem (FSP) together with vehicle technology selection for bus fleet sizing and management. A decision support tool was developed that combines a multi-criteria decision analysis, using the Analytic Hierarchy Process (AHP), and an enumeration procedure. The tool assists transportation operators in selecting optimal frequencies and vehicle technologies, considering economic, social, and environmental criteria. Computational experiments performed in the city of Niterói, Brazil, demonstrate the effectiveness of the tool. Scenarios with different criteria prioritizations highlight the flexibility of the approach and emphasize the need for a balance between all the sustainability dimensions. This approach positively impacts public transportation system performance, favouring higher-capacity vehicles while considering demand, and contributing to sustainable urban mobility. © 2024 The Authors.

Abstract

Abstract
This work analyzes the sensitivity of an optical system consisting of two fiber Fabry-Perot ( FP) interferometers and the apparent increase in sensitivity due to the harmonics of the Vernier effect. Two scenarios are examined: (1) when the larger FP cavity acts as the sensor, and (2) when the smaller FP cavity acts as the sensor. The computation analysis reveals that in the first scenario, higher-order spectral harmonics yield greater sensitivity for maxima and minima of the same order. In the second scenario, however, the sensitivity remains constant and does not depend on the harmonic order. Moreover, it is demonstrated that the sensitivity curve is identical for both scenarios, regardless of the harmonic order. This outcome occurs because the use of spectral harmonics simply reduces the free-spectral range in certain situations, bringing the extrema closer to the maximum sensitivity condition (i.e., Delta L = 0) and thereby increasing sensitivity. Consequently, if points on the envelope other than maxima or minima are used, the sensitivity achieved is the same for both scenarios.

Abstract