Abstract
The present letter proposes the implementation of Vernier-effect harmonics through the virtualization of different reference cavities. A Fabry-Perot interferometer (FPI), actuated by a piezoelectric transducer (PZT), was employed as the sensing element. Subsequently, the sensitivity of the dynamic range was investigated for both the individual interferometer and the implementation of the Virtual Vernier effect. A sensitivity of (8 +/- 0.05)x10(-3) nm/nm was achieved for the single sensor measurement. Considering the implementation of the Vernier effect, the following sensitivities were obtained: (65.6 +/- 0.08)x10(-3) nm/nm for the fundamental, (132 +/- 1)x10-3 nm/nm for the first harmonic, and (192 +/- 1)x10(-3) nm/nm for the second harmonic. Furthermore, a maximum dynamic range of 11.25 mu m and a maximum resolution of 5 pm were achieved. This study highlights the advantages of simultaneously measuring both a single sensor cavity and a harmonic of the Virtual Vernier effect, in order to achieve large dynamic ranges along with high resolution.

Abstract
High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a pose-graph SLAM and calibration framework specifically designed for 3D profiling sonars, such as the Coda Octopus Echoscope 3D. The system integrates a probabilistic scan matching method (3DupIC) for direct registration of 3D sonar scans, enabling accurate trajectory and map estimation even under degraded dead reckoning conditions. Unlike other bathymetric SLAM methods that rely on submaps and assume short-term localization accuracy, the proposed approach performs direct scan-to-scan registration, removing this dependency. The factor graph is extended to represent the sonar extrinsic parameters, allowing the sonar-to-body transformation to be refined jointly with trajectory optimization. Experimental validation on a challenging real world dataset demonstrates outstanding localization and mapping performance. The use of refined extrinsic parameters further improves both accuracy and map consistency, confirming the effectiveness of the proposed joint SLAM and calibration approach for robust and consistent underwater mapping.

Abstract
Traffic flow prediction remains a complex task due to the intricate spatial and temporal correlations in real-world traffic data. Although existing graph neural network (GNN) approaches have shown promise in capturing these relationships, their high computational requirements limit their suitability for real-time deployment. To overcome these limitations, we propose spatiotemporal adaptive refinement with knowledge distillation (STARK), a novel and efficient framework that integrates graph fusion with adaptive knowledge distillation (AKD) in a spatiotemporal graph convolutional network (STGCN). Our method leverages graph fusion to capture both localized and global traffic dynamics, enhancing adaptability across diverse traffic conditions. It further employs two dedicated teacher models that independently emphasize spatial and temporal features, guiding a lightweight student model through a distillation process that dynamically adjusts based on prediction uncertainty. This adaptive learning mechanism enables the student model to prioritize and better learn from more difficult prediction instances. Evaluations on four benchmark traffic datasets [PEMS03, PEMS04, PEMSD7(M), and PEMS08] demonstrate that STARK achieves competitive predictive performance, measured by mean absolute error (MAE) and root mean square error (RMSE), while significantly reducing computational overhead. Our approach thus offers an effective and scalable solution for real-time traffic forecasting.

Abstract

Abstract
The concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.

Abstract
Time series data are essential for a wide range of applications, particularly in developing robust machine learning models. However, access to high-quality datasets is often limited due to privacy concerns, acquisition costs, and labeling challenges. Synthetic time series generation has emerged as a promising solution to address these constraints. In this work, we present a framework for generating synthetic time series by leveraging complex networks mappings. Specifically, we investigate whether time series transformed into Quantile Graphs (QG) -- and then reconstructed via inverse mapping -- can produce synthetic data that preserve the statistical and structural properties of the original. We evaluate the fidelity and utility of the generated data using both simulated and real-world datasets, and compare our approach against state-of-the-art Generative Adversarial Network (GAN) methods. Results indicate that our quantile graph-based methodology offers a competitive and interpretable alternative for synthetic time series generation.