Abstract
In an industrial setting, predicting the remaining useful life-time of equipment and systems is crucial for ensuring efficient operation, reducing downtime, and prolonging the life of costly assets. There are state-of-the-art machine learning methods supporting this task. However, in this paper, we argue, that both efficiency and understandability can be improved by the use of explainable AI methods that analyze the importance of features used by the machine learning model. In the paper, we analyze the feature importance before a failure occurs to identify events in which an increase in importance can be observed and based on that indicate attributes with the most influence on the failure. We demonstrate how the analyses of Shap values near the occurrence of failures can help identify the specific features that led to the failure. This in turn can help in identifying the root cause of the problem and developing strategies to prevent future failures. Additionally, it can be used to identify areas where maintenance or replacement is needed to prevent failure and prolong the useful life of a system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Immersive audio technologies have broadened postproduction strategies for spatial audio, gaining popularity among mainstream audiences. However, there is a lack of defined procedures and critical thinking regarding audio mixing guidelines for surround sound in popular music. In this context, we conducted an empirical study to identify trends concerning instrument position, trajectories, and dynamics from surround mixings. Furthermore, we assess the degree to which they differ from their stereo renderings. Seven award-winning songs in the Grammy category for Best Immersive Album were analyzed, including surround 5.1 and stereo versions. The study found consistent instrument positions in the songs, with rhythmic instruments and bass in the center, lead vocals spread across front channels, and harmonic instruments in wider positions. Solo instruments occupied left, right, and center channels, with dynamics emphasizing lead vocals and solos. Trajectories were rarely used, indicating channel-based thinking. Limited adoption of immersive audio dimensions and reliance on stereo techniques were observed, with no notable differences between the surround and stereo versions. Identified song outliers are discussed and offer avenues for exploration, highlighting the importance of diverse musical expressions in informing immersive audio mixing. © 2023 IEEE.

Abstract
Sustainable mobility has become one of the most pressing issues in modern society. The need to raise awareness of climate change, combined with the overcrowding of metropolitan and urban areas, has produced a situation that requires an urgent solution. Some earlier approaches dealt primarily with transport-related issues, while some conceptual models attempted to increase the appeal of public transport by linking the services provided by public transport operators to a variety of city services. A practical and empirical answer, on the other hand, has not yet been given. This research addrebes these issues by taking a holistic approach and presenting a personalized recommendation system based on users' everyday activities as well as their mobility profiles. The crossing of both sources of information allows for a more user-centric experience, ensuring that the offers presented are adapted to the tastes of customers. The potential of such a system is proven using data from Porto, Portugal. Two types of data sources were used to obtain more accurate results: data from the automated fare collection system of the Porto Metropolitan Area, Portugal, and data from city services taken from Google Places. The fundamental idea behind tackling this problem is to encourage people to use public transport by providing them with incentives such as discounts, promotions and service offers to encourage them to use cleaner and more efficient modes of transport. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
Human-Robot Collaboration is a critical component of Industry 4.0, contributing to a transition towards more flexible production systems that are quickly adjustable to changing production requirements. This paper aims to increase the natural collaboration level of a robotic engine assembly station by proposing a cognitive system powered by computer vision and deep learning to interpret implicit communication cues of the operator. The proposed system, which is based on a residual convolutional neural network with 34 layers and a long -short term memory recurrent neural network (ResNet-34 + LSTM), obtains assembly context through action recognition of the tasks performed by the operator. The assembly context was then integrated in a collaborative assembly plan capable of autonomously commanding the robot tasks. The proposed model showed a great performance, achieving an accuracy of 96.65% and a temporal mean intersection over union (mIoU) of 94.11% for the action recognition of the considered assembly. Moreover, a task-oriented evaluation showed that the proposed cognitive system was able to leverage the performed human action recognition to command the adequate robot actions with near-perfect accuracy. As such, the proposed system was considered as successful at increasing the natural collaboration level of the considered assembly station.

Abstract
The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

Abstract
We study the impact of sound design – soundscape, sound effects, and auditory notifications, namely earcons – on the player’s experience of serious games. Three sound design versions for the game Venci’s Adventures have been developed: 1) no sound; 2) standard sound design, including soundscapes and sound effects; and 3) standard sound design with auditory notification (namely, earcons). Perceptual experiments were conducted to evaluate the most suitable attention retention earcons from a diverse collection of timbres, pitch, and melodic patterns, as well as the user experience of the different sound design versions assessed in pairs (1 vs. 2 and 2 vs. 3). Our results show that participants (n= 23 ) perceive better user experience in terms of game playing competence, immersion, flow, challenge and affect, and enhanced attention retention when adopting standard sound design with the earcons. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.