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.

Abstract
The increasing reliance of modern power systems on heterogeneous renewable energy and decreasing contribution of inertial thermal resources necessitates the availability of planning tools to ensure the continued operational stability of these systems. The abundance of historical data allows the estimation of behaviour during contingencies in common network configurations, but overlooks feasible but rare combinations of generation. Surveys of operation levels at regular intervals can ignore critical areas of operation without high resolution, which requires a significant computational overhead. This paper seeks to address the need for reliable dynamic security assessment to inform grid operator decisions on contingency planning. The aim is to demonstrate the creation of an off-line database that surveys the possible network operation configurations drawing on statistical historical analysis and efficient generic sampling. A high degree of accuracy is achieved in identifying energy mixes that can be expected to result in unstable operation during an unanticipated network outage through the implementation of importance sampling.

Abstract
The rehabilitation of burn patients is essential and is intrinsically linked to conventional rehabilitation; the motivational challenges faced by burn patients in maintaining engagement with these rehabilitation programs are well known. It is understood that the use of other resources, particularly technological ones, associated with conventional rehabilitation could overcome these constraints and thereby optimize the rehabilitation program and health outcomes. The objective of this study is to synthesize the available evidence on the use of exergames in rehabilitation programs for burn patients. This systematic review was developed following the guidelines of the Joanna Briggs Institute (JBI). The search was conducted in the following databases: Medline (R), CINAHL (R), Sports Discus (R), Cochrane (R), and Scopus (R) during May 2025. The PRISMA Checklist Model was used to organize the information from the selected studies. Seven RCTs were included, involving a total of 236 participants. Outcomes related to the use of exergames in the rehabilitation of burn patients were identified, including increased range of motion, functionality, strength, speed of movement, improved balance, reduced fear and pain, and satisfaction with the technological resource used. It is believed that the results of this review, which confirmed the advantage of using exergames, such as Nintendo Wii, PlayStation, Xbox Kinect, or Wii Fit, to optimize the functionality of burn patients, can support clinical decision-making and encourage the integration of exergames to improve rehabilitation programs for burn patients.

Abstract
Building on theories of human sound perception and spatial cognition, this paper introduces a sonification method that facilitates navigation by auditory cues. These cues help users recognize objects and key urban architectural elements, encoding their semantic and spatial properties using non-speech audio signals. The study reviews advances in object detection and sonification methodologies, proposing a novel approach that maps semantic properties (i.e., material, width, interaction level) to timbre, pitch, and gain modulation and spatial properties (i.e., distance, position, elevation) to gain, panning, and melodic sequences. We adopt a three-phase methodology to validate our method. First, we selected sounds to represent the object’s materials based on the acoustic properties of crowdsourced annotated samples. Second, we conducted an online perceptual experiment to evaluate intuitive mappings between sounds and object semantic attributes. Finally, in-person navigation experiments were conducted in virtual reality to assess semantic and spatial recognition. The results demonstrate a notable perceptual differentiation between materials, with a global accuracy of .69 ± .13 and a mean navigation accuracy of .73 ± .16, highlighting the method’s effectiveness. Furthermore, the results suggest a need for improved associations between sounds and objects and reveal demographic factors that are influential in the perception of sounds.

Abstract