Abstract
This paper presents the development of an automated water sampling system designed to enhance quality control in water treatment facilities. The system is built around a mechanical structure that houses a watertight box containing all electronic components. A display inside the box allows users to program sampling schedules, including parameters such as the day, time, number of samples, sample volume, and intervals between samples. A balance integrated into the structure holds a bottle that collects the water samples, while a reservoir at the bottom accumulates water to ensure an adequate supply. A water pump connected to the structure enables controlled sample collection. The design ensures that all components are compactly integrated, while a non-invasive method is used to measure the volume of the sampled water, thereby avoiding direct contact between the sensors and the sample. A Project-Based Learning (PBL) approach, coupled with direct industry collaboration, has reinforced the effectiveness of active learning methodologies in engineering education.

Abstract
The implementation of Onshore Power Supply (OPS) systems is recognised as an effective strategy to reduce air emissions and noise pollution from ships at berth, thereby supporting decarbonization goals established by the European Union. Despite its environmental advantages, the economic viability of OPS remains one of the main barriers to its widespread adoption in ports. In particular, limited research attention has paid to the extent to which the service level provided by OPS infrastructure affects the financial performance of such investments.This paper addresses this gap by analysing the relationship between service level and economic viability through a case study of the Southern Container Terminal at the Port of Leixões (Portugal). Three investment scenarios were evaluated, corresponding to the installation of one, two, and three OPS systems, delivering service levels of 65%, 94%, and 100%, respectively. A 20-year financial analysis (2025–2044) was conducted using Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period as key indicators, under discount rates of 3% and 5%.The results demonstrate that service level significantly influences investment feasibility. The scenario with a 65% service level is economically unviable, presenting negative NPVs and an IRR below the cost of capital. In contrast, the 94% and 100% service level scenarios generate positive NPVs (at 3%) and IRRs slightly above 5%, indicating moderate but acceptable profitability. However, increasing the service level from 94% to 100% does not produce a proportional improvement in financial returns, revealing diminishing marginal gains relative to the intermediate scenario.The findings indicate that maximising service level does not necessarily maximise economic attractiveness. Instead, an optimal balance between infrastructure capacity and expected demand is essential to ensure financial sustainability while delivering substantial environmental benefits. These findings offer practical guidance for port authorities and policymakers seeking to design economically viable OPS systems.

Abstract
A novel technique based on multiple amplitude wavelength modulation spectroscopy (MA-WMS) for simultaneous measurement of CH4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {CH}_4$$\end{document} gas concentration and pressure was developed and validated both through simulation and experiment, showing good agreement. To capture the spectrum broadening caused by increasing pressure and concomitantly obtain the concentration at the sensor's location, a laser centered at 1650.9 nm was subjected to multiple amplitude modulation depths while the 2fm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2f_{m}$$\end{document} signal, normalized by the DC component (an invariant quantity under optical loss), was recorded. While the use of a single and fixed modulation can introduce an ambiguity, as different pairs of pressure and concentration can yield the same value, this ambiguity is eliminated by employing multiple amplitude modulations. In this approach, the intersection point of the three level curves can provide the local pressure and concentration. The proposed system was able to measure concentrations from 5% up to 45% and pressures from 0.25 atm up to 1.75 atm, with a maximum error of 2% in concentration and 0.06 atm in pressure, respectively. The system was also tested for attenuation insensitivity, demonstrating that measurements were not significantly affected for up to 10 dB applied optical loss.

Abstract
This paper explores an innovative distributed real-time control system for a 3D-printed robotic leg. The system is constructed on a modular multi-board architecture that seamlessly integrates with ROS2 and micro-ROS, demonstrating the use of 3D printing for rapid prototyping and customized solutions. A notable feature of this robotic leg is its 360-degree rotating joint, which extends its range of motion, enabling intricate and versatile movements. Incorporating a shoulder joint further facilitates sideways mobility, augmenting its operational capabilities. A multi-board architecture is designed to ensure efficient communication, ease of component interchangeability, and robust scalability for future development. Additionally, advanced control techniques, including tuning of proportional-integral-derivative (PID) controllers, ensure responsive joint actuation tailored to the unique properties of 3D-printed materials. Experimental validation indicates low latency and stable operation, underscoring the system's effectiveness for real-time robotic applications.

Abstract
Macroeconomic forecasting is a fundamental domain for policy decisions, directly impacting the whole population of a country. The use of machine learning (ML) approaches in economics forecasting has been studied in several types of research in the academic field, aiming to improve or even replace traditional econometric approaches. However, the use of ML in forecasting is now getting closer to policy markers, which are the institutions that make policy decisions. Three relevant studies are presented and analyzed in this work; all focused on forecasting using ML of different macroeconomic variables in several economies. The studies were compared, including aspects of methodologies and results, as well as similarities and differences. In addition, several technical, legal, and philosophical questions were raised regarding the effective use of data from ML forecasting in public policies, including topics related to the standardization of the research on this topic, the explanation of the model's output, protection of trust, and ethics issues.

Abstract
Web frontends are ubiquitous, from Web pages and single-page applications to hybrid mobile apps, Web frontends play a crucial role in today’s digital economy. At their core, they rely on JavaScript, whose single-threaded nature poses significant challenges to delivering smooth and responsive user experiences as complexity rises. In–browser parallelism promises responsiveness and throughput improvements, but spans several mechanisms ( Web Workers , Worklets , OffscreenCanvas , WebAssembly threads ) with diverse coordination models. We conducted a systematic review of primary studies on using Web Workers for parallel JavaScript in browsers, extracting design and scope choices, to collate the resources necessary for a generic ES5-compatible parallel enumeration system capable of type introspection. Such a system would enable widespread in-browser parallelization without any external plug-ins or experimental JavaScript specifications, and spare developers from managing work-splitting and result-merging.