Abstract
Hydrogen (H2) detection has become extremely important in recent years due to the increasing need for sustainable alternative energy sources. In this field, optical sensors can contribute significantly due to remote interrogation capabilities and the absence of ignition sources. Among the different H2 optical sensors, plasmonic sensors appear to be a very sensitive technology; however, they require expensive plasmonic materials like gold or silver, which, together with a palladium-sensitive layer, can increase the sensor cost. In addition, plasmonic bands are usually outside the ideal infrared range for remote interrogation, between 1500 and 1600 nm. This work presents a polymer-protected Tamm Plasmon Resonance (TPR) sensor with a well-defined resonance band at 1572 nm composed of SiO2, TiO2 layers, and palladium as a sensitive layer. This architecture can reduce the production cost of sensing structures, replacing plasmonic films with dielectric materials, while offering improved resonance definition at longer wavelengths. First, numerical calculations were carried out using the Transfer-Matrix Method to study the impact of the thickness of each layer, incidence angle, and light polarization on the resonance band wavelength and H2 sensitivity. The optimized structure was then fabricated, exhibiting a wavelength shift of 9.5 nm to 4 vol % of H2, a response time of 30 s, and no cross-sensitivity to methane or ammonia. The sensor also demonstrated high stability and resistance to environmental degradation up to eight days. These results emphasize the advantages of TPR structures for gas sensing in the infrared spectral range, opening new avenues for remote plasmonic sensing. © 2026 The Authors. Published by American Chemical Society

Abstract
Eye diseases can have highly adverse outcomes without an early diagnosis and correct monitoring. Retinopathy of Prematurity (ROP) Plus Form, in particular, is a disease that can lead to childhood blindness, and its diagnosis requires medical experts to examine the retinal condition manually. Although developments in screening equipment have helped, this is still a time-consuming and subjective task. The development of automatic tools for Retinal Blood Vessel Segmentation allows the extraction of blood vessels from fundus images, which healthcare experts can then use to perform the diagnosis, monitoring, and prognosis of eye diseases. Thus, developing such a segmentation tool is a widely explored task with different methodologies that can be followed. However, many studies try to segment all the blood vessels rather than only the most important ones. In this work, we present a segmentation pipeline to segment only the main vessels whose characteristics can be used to assess ROP Plus Form disease. This pipeline uses different operations and filters, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and Frangi Filtering. The final segmentation is done by determining a threshold value using the Triangle Threshold algorithm. The pipeline was tested in the well-known DRIVE Database, achieving an Accuracy of 0.949, Specificity of 0.963, and Sensitivity of 0.756.

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
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.