Abstract
Self and co-regulation of learning (SCRL) are strategies that students can adopt to become more active and committed to their learning. Encouraging students to adopt these strategies is a challenge for teachers that can be met by using narratives as a teaching resource. To support teachers in this process, we present a method for evaluating, classifying, and reflecting on excerpts from immersive narratives for SCRL, so they objectively base their decision-making. The method was developed as an artifact of Design Science Research (DSR). In the Design stage of DSR, a 4-stage scheme was developed, and 38 criteria were described to identify and classify narratives that guide or encourage students to adopt SCRL strategies. In the DSR demonstration stage, we tested the method in an asynchronous e-learning curricular unit in Portuguese higher education, which uses a narrative-oriented immersive learning approach for SCRL, called e-SimProgramming. The results show that the graphic visualization of the classification made it possible to perceive the occurrence of the SCRL categories in the narratives, enabling the teacher to be inspired and reflect on the categories to be enhanced for necessary changes in the narrative in line with their pedagogical objectives.

Abstract
Detecting polyps in endoscopic images is essential in healthcare, requiring Explainable Artificial Intelligence (XAI) techniques to ensure transparency and confidence in AI models. Example-based XAI approaches, such as Anchors and Integrated Gradients, are promising but still need to be explored to improve the interpretability of models. In this study, a model was developed that achieved 91% accuracy on the test set. Anchors provided clear and intuitive explanations by highlighting critical regions, such as the polyp area, making it easier for clinical experts to understand the model's decisions. Integrated Gradients offered a detailed pixel-by-pixel analysis, covering the polyp area and other parts of the image, providing a comprehensive view of the model's behaviour. The comparative analysis revealed that Anchors are particularly useful for clarity, while Integrated Gradients offer greater depth and granularity. The combined use of these techniques improves the interpretability of AI models, increasing confidence and acceptance in critical healthcare applications and supporting informed clinical decisions. © 2025 The Author(s).

Abstract
Abstract In this article, we formalize and analyze qualia motion, i.e., the process by which a composition transitions across distinct harmonic qualities through the Fourier qualia space (FQS)—a multidimensional and transposition-independent space based on the discrete Fourier transform (DFT) coefficients’ magnitude. In the FQS, the plot of set classes relies on their harmonic qualities—such as diatonicity and octatonicity—enabling us to (1) identify the pitch-class set in a musical phrase that best represents its qualia—a reference sonority; (2) define a harmonic progression using all sequential reference sonorities in a piece; (3) visualize trajectory in space; and (4) establish a statistical metric for the ambiguity of harmonic qualia. Finally, we discuss Schoenberg's Op. 19, No. 1, analyzing the sense of its harmonic path. The proposed space leverages a bipartite, symmetrical, and consequential structure and unveils ambiguity as an element of nondirected linearity and counterpoint.

Abstract
State-of-the-art symbolic music generation models have recently achieved remarkable output quality, yet explicit control over compositional features, such as tonal tension, remains challenging. We propose a novel approach that integrates a computational tonal tension model, based on tonal interval vector analysis, into a Transformer framework. Our method employs a two-level beam search strategy during inference. At the token level, generated candidates are re-ranked using model probability and diversity metrics to maintain overall quality. At the bar level, a tension-based re-ranking is applied to ensure that the generated music aligns with a desired tension curve. Objective evaluations indicate that our approach effectively modulates tonal tension, and subjective listening tests confirm that the system produces outputs that align with the target tension. These results demonstrate that explicit tension conditioning through a dual-level beam search provides a powerful and intuitive tool to guide AI-generated music. Furthermore, our experiments demonstrate that our method can generate multiple distinct musical interpretations under the same tension condition.

Abstract
Hydrogen (H-2) is a promising alternative to fossil fuels. However, safety concerns need constant monitoring. Fiber optical sensors have become crucial in this field due to their capability for remote measurements. Traditional plasmonic techniques applied on optical fibers rely on expensive materials, which implies removing the fiber protection, and the optimized bands are outside the infrared spectral range preferred in optical communications. To address these challenges, this work presents an alternative plasmonic structure at the fiber tip of a single-mode fiber. The approach is based on Tamm Plasmon Resonance (TPR), which can be excited at normal incidence with depolarized light. Numerical results indicate that the numerical aperture of the fiber has minimal impact on the TPR band. Experimental results validate the possibility of this approach for H-2 detection, showing a wavelength shift of 8.5nm for 4 vol% H-2 with the TPR band centered around 1565nm. The sensor presents a response time of 29s and a reset time of 27s. These findings open new avenues in the development of plasmonic optical fiber sensors for H-2 sensing, as they enable the possibility of exciting plasmonic modes without removing the fiber's cladding and with simple structures.

Abstract
Optical Wireless Communications (OWC) has recently emerged as a viable alternative to radio-frequency technology, especially for the Internet of Things (IoT) domain. However, current simulation tools primarily focus on physical layer modelling, ignoring network-level issues and energy-constrained environments. This paper presents an energy-aware OWC module for ns-3 that addresses these limitations. The module includes specific PHY and MAC layers and integrates an energy model, a mobility model, and models of monochromatic transceivers and photodetectors, supporting both visible light and infrared (IR) communications. Verification against MATLAB simulations confirms the accuracy of our implementation. Additionally, mobility tests demonstrate that an energy-restricted end device transmitting via IR can maintain a stable connection with a gateway at distances up to 2.5 m, provided the SNR is above 10 dB. These results confirm the capabilities of our module and its potential to facilitate the development of energy-efficient OWC-based IoT systems.