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Abstract
A refractive index sensor was designed using a novel approach to sensing based on a cleaved standard fiber Bragg grating (FBG) at the grating region, which enables the FBG to interact with its surrounding environment. The sliced-FBG (SFBG) exhibits a variable phase shift in the reflection response due to the length of the last grating's pitch, which differs from the rest. At the SFBG, the signal is the result of interference between the reflected wave from the grating and the transmitted spectrum returned due to Fresnel reflection at the final pitch, and the intensity of this signal depends on the refractive index of the surrounding medium. Based on this phenomenon, an intensity-based refractive index sensor with self- referencing technique was employed in this experiment, whereby the grating peak maximum point served as the signal reference, while the minimum of the Fresnel peak from each measurement functioned as the signal input. The proposed sensor demonstrated the ability to measure refractive indices within the range of 1.333-1.339, with a resolution of approximate to 10(-3), and a minimum detectable value of 6x10(-4) RIU (the data yielded a linear response with R-2=0.990). This study presents an innovative data sensing approach compared to existing techniques found in literature, which typically employ wavelength variation in the reflected wave to extract the desired information.

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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.

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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.

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