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
Early fault detection remains a critical challenge in predictive maintenance (PdM), particularly within critical infrastructure, where undetected failures or delayed interventions can compromise safety and disrupt operations. Traditional anomaly detection methods are typically reactive, relying on real-time sensor data to identify deviations as they occur. This reactive nature often provides insufficient lead time for effective maintenance planning. To address this limitation, we propose a novel two-stage early detection framework that integrates time series forecasting with anomaly detection to anticipate equipment failures several hours in advance. In the first stage, future sensor signal values are predicted using forecasting models; in the second, conventional anomaly detection algorithms are applied directly to the forecasted data. By shifting from real-time to anticipatory detection, the framework aims to deliver actionable early warnings, enabling timely and preventive maintenance. We validate this approach through a case study focused on metro train systems, an environment where early fault detection is crucial for minimizing service disruptions, optimizing maintenance schedules, and ensuring passenger safety. The framework is evaluated across three forecast horizons (1, 3, and 6 hours ahead) using twelve state-of-the-art anomaly detection algorithms from diverse methodological families. Detection performance is assessed using five performance metrics. Results show that anomaly detection remains highly effective at short to medium horizons, with performance at 1-hour and 3-hour forecasts comparable to that of real-time data. Ensemble and deep learning models exhibit strong robustness to forecast uncertainty, maintaining consistent results with real-time data even at 6-hour forecasts. In contrast, distance- and density-based models suffer substantial degradation at longer horizons (6-hours), reflecting their sensitivity to distributional shifts in predicted signals. Overall, the proposed framework offers a practical and extensible solution for enhancing traditional PdM systems with proactive capabilities. By enabling early anomaly detection on forecasted data, it supports improved decision-making, operational resilience, and maintenance planning in industrial environments.

Abstract
Robotic bin-picking is a critical operation in modern industry, which is characterised by the detection, selection, and placement of items from a disordered and cluttered environment, which can be boundary limited or not, e.g. bins, boxes or containers. In this context, perception systems are employed to localise, detect and estimate grasping points. Despite the considerable progress made, from analytical approaches to recent deep learning methods, challenges still remain. This is evidenced by the growing innovation proposing distinct solutions. This paper aims to review perception methodologies developed since 2009, providing detailed descriptions and discussions of their implementation. Additionally, it presents an extensive study, detailing each work, along with a comprehensive overview of the advancements in bin-picking perception.

Abstract
With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development—both known to boost productivity and efficiency in software development—an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.

Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation (DT), the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realizing the benefits of Digital Transformation (DT). This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises (SMEs) through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises (SMEs) in the Digital Transformation (DT) process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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.