Abstract
The Internet -of -Things (IoT) has created a complex environment where hardware and software interact in complex ways. Despite being a prime candidate for applying well -established software engineering practices, IoT has not seen the same level of adoption as other areas, such as cloud development. This discrepancy is even more evident in the case of edge devices, where programming and managing applications can be challenging due to their heterogeneous nature and dependence on specific toolchains and languages. However, the emergence of WebAssembly as a viable solution for running high-level languages on some devices presents an opportunity to streamline development practices, such as DevOps. In this paper, we present WASMICO - a firmware and command -line utility that allows for the execution and management of application lifecycles in microcontrollers. Our solution has been benchmarked against other state-of-the-art tools, demonstrating its feasibility, novel features, and empirical evidence that it outperforms some of the most widely used solutions for running high-level code on these devices. Overall, our work aims to promote the use of wellestablished software engineering practices in the IoT domain, helping to bridge the gap between cloud and edge development.

Abstract
Virtual Reality (VR) has proven to be an important contribution to tourists' decision-making regarding a destination. This fact can be determinant, especially when tourists face some social limitation or restriction that conditions their participation in tourism activities. Therefore, we aim to understand whether the possibility of experiencing immersive wine tourism activities can encourage future visits, as well as the recommendation of the VR experience and the destination itself. To achieve our goal, we offered 405 participants an experimental VR experience with digital content about a wine tourism activity. The results showed that participants feel that the VR experience influences their behavioural intention towards the wine tourism destination. The satisfaction felt from the experience leads to a significant effect on the intention to visit and to recommend the destination and the VR activity. These findings suggest to wine tourism destination managers that VR can play an essential role in tourism management.

Abstract
The potential of ultrafast laser machining for the design of integrated optical devices in ULE (R) glass, a material known for its low coefficient of thermal expansion (CTE), is addressed. This was done through laser direct writing and characterization of optical waveguides and through the fabrication of 3D cavities inside the glass by following laser irradiation with chemical etching. Type I optical waveguides were produced and their internal loss mechanisms at 1550 nm were studied. Coupling losses lower than 0.2 dB cm-1 were obtained within a wide processing window. However, propagation loss lower than 4.2-4.3 dB cm-1 could not be realized, unlike in other glasses, due to laser-induced photodarkening. Selective-induced etching was observed over a large processing window and found to be maximum when irradiating the glass with a fs-laser beam linearly polarised orthogonally to the scanning direction, akin to what is observed in fused silica laser-machined microfluidic channels. In fact, the etching selectivity and surface roughness of laser-machined ULE (R) glass was found to be similar to that of fused silica, allowing some of the already reported microfluidic and optofluidic devices to be replicated in this low CTE glass. An example of a 3D cavity with planar-spherically convex interfaces is given. Due to the thermal properties of ULE (R) glass, these cavities can be employed as interferometers for wavelength and/or temperature referencing.

Abstract
This paper present an instance transformation procedure to modify known instances of the resource -constrained project scheduling problem to make them easier to solve by heuristic and/or exact solution algorithms. The procedure makes use of a set of transformation rules that aim at reducing the feasible search space without excluding at least one possible optimal solution. The procedure will be applied to a set of 11,183 instances and it will be shown by a set of experiments that these transformations lead to 110 improved lower bounds, 16 new and better schedules (found by three meta -heuristic procedures and a set of branch -and -bound procedures) and even 64 new optimal solutions which were never not found before.

Abstract
An algorithm for blind source separation (BSS) of the second heart sound (S2) into aortic and pulmonary components is proposed. It recovers aortic (A2) and pulmonary (P2) waveforms, as well as their relative delays, by solving an alternating optimization problem on the set of S2 sounds, without the use of auxiliary ECG or respiration phase measurement data. This unsupervised and data-driven approach assumes that the A2 and P2 components maintain the same waveform across heartbeats and that the relative delay between onset of the components varies according to respiration phase. The proposed approach is applied to synthetic heart sounds and to real-world heart sounds from 43 patients. It improves over two state-of-the-art BSS approaches by 10% normalized root mean-squared error in the reconstruction of aortic and pulmonary components using synthetic heart sounds, demonstrates robustness to noise, and recovery of splitting delays. The detection of pulmonary hypertension (PH) in a Brazilian population is demonstrated by training a classifier on three scalar features from the recovered A2 and P2 waveforms, and this yields an auROC of 0.76.

Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.