Abstract
Optical sensing exploiting plasmonics and other types of surface waves provides exceptional performance for chemical and biological detection due to its high sensitivity and real-time capabilities. This study explores the integration of thin films with plasmonic, specifically leveraging metallic and dielectric nano structures, fabricated through sputtering and colloidal synthesis techniques. Advanced surface wave excitations such as localized surface plasmon resonances (SPR), Tamm Plasmon Polaritons (TPP), Bloch surface waves, and surface plasmon polaritons (SPP) are used to amplify sensor performance. Simulations and experimental data show that these nanostructured coatings significantly enhance electromagnetic field confinement, leading to improved detection limits and sensor robustness, showcasing promising applications in environmental monitoring, gas detection, and biomedical diagnostics.

Abstract
Obstructive sleep apnea (OSA) is a sleep disorder marked by repeated episodes of airway obstruction, leading to apneas (complete blockage) or hypopneas (partial blockage) during sleep. The standard diagnostic metric, the apnea-hypopnea index (AHI), quantifies the number of these events per hour of sleep but has limitations, such as its dependence on manual interpretation and lack of attention to event duration, which can be clinically significant. To address these issues, this study developed an algorithm to detect respiratory events from nasal airflow signals and measure their duration, using data from 22 patients at St. Vincent's University Hospital, sourced from the PhysioNet dataset. Signal processing techniques, including filtering and envelope analysis, were applied to extract features, and apnea/hypopnea events were identified based on American Academy of Sleep Medicine (AASM) guidelines. Events were classified by duration into three groups: 10-20 seconds, 20-40 seconds, and over 40 seconds. Preliminary results showed detection accuracy of 60% for apnea and 93% for hypopnea events. The study also explored relations between event duration and demographic factors, such as age, gender, body mass index (BMI), and Epworth Sleepiness Scale (ESS) scores, to assess whether longer events were linked to greater severity. These findings suggest that incorporating event duration and automated detection into OSA diagnosis could improve accuracy and provide better insight into the condition, potentially leading to more personalized treatments.

Abstract
In an increasingly competitive tourism landscape, digital marketing strategies must adapt to engage modern travelers effectively. This paper investigates the role of gamification in enhancing the competitiveness of tourist destinations, focusing specifically on Braga, Portugal. The study aims to evaluate how gamification can influence destination attractiveness, enhance tourist experiences, and inform decision-making processes. The study used a mixed-methods approach, combining qualitative and quantitative analyses. The qualitative component consisted of semistructured interviews with key tourism stakeholders in Braga, revealing insights into the benefits and challenges of implementing gamification strategies. The quantitative analysis involved surveys conducted with tourists, assessing their perceptions of gamified elements and their influence on travel decisions. Findings indicate that tourists perceive gamification positively, particularly regarding features such as achievements, storytelling, and point systems, which significantly affect their travel choices. Stakeholders recognize the potential of gamification to boost tourist engagement and satisfaction, while also emphasizing the need to address implementation challenges. The study concludes that gamification can enhance the attractiveness and competitiveness of tourist destinations, though its success depends on strategic planning, resource allocation, and collaboration among stakeholders.

Abstract
Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository.

Abstract
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. These compressed models are especially essential when implementing multi-model fusion solutions where multiple models are required to operate simultaneously. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

Abstract
Computational reproducibility remains a critical yet unresolved issue across scientific disciplines, often hindered by complex configuration requirements and technical barriers. We present SciConv, a novel conversational tool designed to assist researchers in creating and executing reproducible computational experiments using natural language. By leveraging large language models (llMs), SciConv automates the detection of dependencies and programming languages, and packages experiments into portable artifacts with minimal manual input. Unlike traditional platforms based on graphical user interfaces (e.g., web-based), SciConv features a chat-based interface that guides researchers interactively through the reproducibility workflow. This paper introduces the architecture, design principles, and interaction model of SciConv, and discusses its potential to lower the technical barriers to reproducibility.