Abstract
Eating behavior adopted during adolescence may persist into adulthood. The aims of this study were to identify eating behavior patterns among Portuguese adolescents and to explore whether groups differ in terms of early life and family characteristics, severity of depressive symptoms, and body mass index (BMI) z-score. Participants were 3601 13-year-olds enrolled in the birth cohort Generation XXI. Eating behavior was assessed using the self-reported Adult Eating Behavior Questionnaire (AEBQ), validated in this sample. The severity of depressive symptoms was measured through the Beck Depression Inventory (BDI-II), and data on sociodemographic and anthropometrics were collected at birth and 13-years-old. Latent class analysis was conducted, and associations were estimated using multinomial logistic regression models. Five patterns of individuals were identified: Picky eating, Disinterest towards food, Food neophilia, Emotional eating, and Food attractiveness. The adolescents' sex, maternal education, BMI z-score, and severity of depressive symptoms were significantly associated with the identified patterns. In particular, adolescents with a higher BMI z-score were more likely in Food neophilia while individuals with more severe depressive symptoms were in the Picky eating, Emotional eating, and Food attractiveness patterns. These findings suggest a starting point for the development and planning of targeted public health interventions.

Abstract
The increase of tissue transparency through sequential optical immersion clearing treatments and treatment reversibility have high interest for clinical applications. To evaluate the clearing reversibility in a broad spectral range and the magnitude of the transparency created by a second treatment, the present study consisted on measuring the spectral collimated transmittance of lung tissues during a sequence of two treatments with electronic cigarette (e-cig) fluid, which was intercalated with an immersion in saline. The saline immersion clearly reverted the clearing effect in the lung tissue in the spectral range between 220 and 1000 nm. By a later application of a second treatment with the e-cig fluid, the magnitude of the optical clearing effect was observed to be about the double as the one observed in the first treatment, showing that the molecules of the optical clearing agent might have converted some bound water into mobile water during the first treatment.

Abstract
An increasing area of study for economists and sociologists is the varying organizational structures between business networks. The use of network science makes it possible to identify the determinants of the performance of these business networks. In this work we look for the determinants of inter-firm performance. On one hand, a new method of supervised clustering with attributed networks is proposed, SUWAN, with the aim at obtaining class-uniform clusters of the turnover, while minimizing the number of clusters. This method deals with representative-based supervised clustering, where a set of initial representatives is randomly chosen. One of the innovative aspects of SUWAN is that we use a supervised clustering algorithm to attributed networks that can be accomplished through a combination of weights between the matrix of distances of nodes and their attributes when defining the clusters. As a benchmark, we use Subgroup Discovery on attributed network data. Subgroup Discovery focuses on detecting subgroups described by specific patterns that are interesting with respect to some target concept and a set of explaining features. On the other hand, in order to analyze the impact of the network's topology on the group's performance, some network topology measures, and the group total turnover were exploited. The proposed methodologies are applied to an inter-organizational network, the EuroGroups Register, a central register that contains statistical information on business networks from European countries.

Abstract
Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging. A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The modern manufacturing environment has adjusted to technological improvements. With Virtual Reality applications geared for factory training are becoming increasingly common. The industry is seeking ways to lower downtimes, resource component waste, risk of possible work accidents and decrease expenses, which can be achieved by engaging in new techniques of training professionals. This article evaluates a VR training application developed within the scope of the R&D project, aimed at training personnel in vehicle antenna production lines. We included the following variables: previous experience with VR technology, cybersickness, immersive tendencies, presence, system usability and satisfaction. Both the system usability scores and satisfaction were considered acceptable. We also found positive correlations between several variables, highlighting the possible influence of attention and familiarity with VR technology on the user experience. In contrast, a negative correlation raised questions about participants' expectations regarding VR technology and their resulting experience.

Abstract
Proper waste management has been recognized as a tool for the green transition towards a more sustainable economy. For instance, most studies dealing with municipal solid wastes in the literature focus on environmental aspects, proposing new routes for recycling, composting and landfilling. However, there are other aspects to be improved in the systems that deal with municipal solid waste, especially in the transportation sector. Scholars have been exploring alternatives to improve the performance in waste collection tasks since the late 50s, for example, considering the waste collection problem as static. The transition from a static approach to a dynamic is necessary to increase the feasibility of the solution, requiring faster algorithms. Here we explore the improvement in the performance of the guided local search metaheuristic available in OR-Tools upon different execution times lower than 10 seconds to solve the capacitated waste collection problem. We show that increasing the execution time from 1 to 10 seconds can overcome savings of up to 1.5 km in the proposed system. Considering application in dynamic scenarios, the 9 s increase in execution time (from 1 to 10 s) would not hinder the algorithm's feasibility. Additionally, the assessment of the relation between performance in different execution times with the dataset's tightness revealed a correlation to be explored in more detail in future studies. The work done here is the first step towards a shift of paradigm from static scenarios in waste collection to dynamic route planning, with the execution time established according to the conclusions achieved in this study. © 2023 ITMA.