Abstract
This paper introduces Motiv, a dataset of expert saxophonist recordings illustrating parallel, similar, oblique, and contrary motions. These motions are variations of three phrases from Jesus VillaRojo's Lamento, with controlled similarities. The dataset includes 116 audio samples recorded by four tenor saxophonists, each annotated with descriptions of motions, musical scores, and latent space vectors generated using the VocalSet RAVE model. Motiv enables the analysis of motion types and their geometric relationships in latent spaces. Our preliminary dataset analysis shows that parallel motions align closely with original phrases, while contrary motions exhibit the largest deviations, and oblique motions show mixed patterns. The dataset also highlights the impact of individual performer nuances. Motiv supports a variety of music information retrieval (MIR) tasks, including gesture-based recognition, performance analysis, and motion-driven retrieval. It also provides insights into the relationship between human motion and music, contributing to real-time music interaction and automated performance systems.

Abstract
Music production has long been characterized by well-defined concepts and techniques. However, a notable gap exists in applying these established principles to music production within immersive media. This paper addresses this gap by examining post-production processes applied to three case studies, i.e., three songs with unique instrumental features and narratives. The primary objective is to facilitate an in-depth analysis of technical and artistic challenges in musical production for immersive media. From a detailed analysis of technical and artistic post-production decisions in the three case studies and a critical examination of theories and techniques from sound design and music production, we propose a framework with a tripartite mixing categorization for immersive media: Traditional Production, Expanded Traditional Production, and Nontraditional Production. These concepts expand music production methodologies in the context of immersive media, offering a framework for understanding the complexities of spatial audio. By exploring these interdisciplinary connections, we aim to enrich the discourse surrounding music production, rethinking its conceptual plane into more integrative media practices outside the core music production paradigm, thus contributing to developing innovative production methodologies. © 2025 IEEE.

Abstract
Many organizations and countries are requiring information systems to innovate and break with their legacy systems in many areas in order to be more effective and stand out from their competitors. The education system is one of those areas in need of innovation, as it is one of the fundamental tools for the success of any society. Due to rapid changes and increased complexity, today's world requires a new educational system that aims to improve the agility and flexibility of students in a highly demanding environment. This study proposes the use of Mixed Reality (MR) in educational systems for teaching STEM subjects to secondary school students, given its proven benefits for both teachers and student performance. Based on a specific case study related to the development of the HoloAnatomy application by CWRU and Cleveland Clinic (USA), as well as the successful learning outcomes of this technology, we present a proposed MR technology to be implemented in the educational system, comprising four subjects and some related content, based on a similar development path.

Abstract
A sensor based on the fiber Bragg grating (FBG) and additive manufacturing for diameter variation measurement is proposed and experimentally demonstrated in this work. Two designs were proposed: a FBG alone and a FBG in series with a spring. Three tests were developed for each design, and at the end, the statistical treatment was performed. The designs were fabricated using a 3D printer, and the FBG sensor is embedded. The results demonstrated that the structures proposed in this work can be used to monitor diameter variation, among other applications. The sensors, with and without spring in series, presented sensitivities of 0.0671 nm/mm and 0.5116 nm/mm, respectively, with a good linear response greater than 0.99.

Abstract
Habitat loss, climate change, and pesticide use are key threats affecting beetle populations. This paper describes Scarabreed, a project that contributes to mitigate the beetle decline crisis. It was carried out by a team of six European students from different engineering fields and nationalities within the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP), a project-based and teamwork learning framework. The designed solution - the Beetle Breeder Version 2 (BBV2) - consists of a smart modular vivarium created especially for beetle breeding. It monitors and controls relevant habitat parameters and offers two user-friendly interfaces (on-device and a Web application). The innovative modular structure of the vivarium allows easy scaling, customisation, and transportation. As a whole, the project offers significant environmental benefits: (i) facilitates the captive breeding of endangered beetle species, promoting population restoration efforts; (ii) fosters, as an educational tool, youth and general public awareness about the crucial role beetles play in ecosystems; and (iii) adopts eco-efficient and responsible business practices by following ethics and sustainability driven design and marketing.

Abstract
Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.