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
As organizations increasingly transition from monolithic systems to microservices, they aim to achieve higher availability, automatic scaling, simplified infrastructure management, enhanced collaboration, and streamlined deployments. However, this migration process remains largely manual and labour-intensive. While existing literature offers various strategies for decomposing monoliths, these approaches primarily focus on architecture-level guidance, often overlooking the code-level challenges and dependencies that developers must address during the migration. This article introduces a catalogue of seven refactorings specifically designed to support the transition to a microservices architecture with a focus on handling dependencies. The catalogue provides developers with a systematic guide that consolidates refactorings identified in the literature and addresses the critical gap in systematizing the process at the code level. By offering a structured, step-by-step approach, this work simplifies the migration process and lays the groundwork for its potential automation, empowering developers to implement these changes efficiently and effectively.

Abstract
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing novel biological insights and therapeutic applications. However, analyzing large amounts of sensitive data raises key data privacy concerns, specifically when the information is outsourced to untrusted third-party infrastructures for data storage and processing (e.g., cloud computing). We introduce Gyosa, a secure and privacy-preserving distributed genomic analysis solution. By leveraging trusted execution environments (TEEs), Gyosa allows users to confidentially delegate their GWAS analysis to untrusted infrastructures. Gyosa implements a computation partitioning scheme that reduces the computation done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, Gyosa provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of Gyosa, reinforcing its ability to provide enhanced security guarantees.

Abstract
In the context of mobile robotics education, realistic and accessible datasets are fundamental for supporting the development and testing of algorithms. However, collecting real-world data is a limited and challenging task because it is time-consuming and error-prone. Therefore, this paper presents the generation of a synthetic dataset through realistic simulation using the SimTwo environment-a physics-based simulator, and modeling techniques of sensors and actuators. The physical and simulated mobile robot was developed to perform tasks such as following a line, following a wall, and avoiding obstacles. The proposed approach facilitates the creation of customized datasets for training and evaluation algorithms while supporting remote and inclusive learning. Results show that a simulated dataset can effectively replicate real-world behaviors, making them a valuable resource for educational contexts, research, and development. Some emergent machine learning algorithms can be applied to this dataset, being this approach increasingly used to enhance robot localization, by leveraging ML, robots can improve the accuracy, robustness, and adaptability of their localization systems, especially in complex and dynamic environments.

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.