Abstract
The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.

Abstract
We propose a refinement process of dynamic equilibria based on small random perturbations (SRPs) of the backward perfect foresight (bpf) equilibrium map in a class of one-step, forward-looking dynamic models. An equilibrium is selected if its stationary measure is the limit of the stationary measures associated with the processes generated by the SRPs of the bpf maps, as the perturbation size approaches 0. We show that, for full measure sets of parameter values of a large class of one-parameter families of unimodal bpf maps, only determinate cycles or the chaotic sunspot equilibrium defined by Araujo and Maldonado (2000) is selected. Two examples are provided illustrating such refinement process.

Abstract
Manuscript type: Research paper Research aims: This study aims to shed light on the factors that facilitate online purchase of perfume. It specifically: 1) explores the antecedents of online purchase behaviour in the case of experience goods; 2) analyses the motivations, perceived benefits and perceived risks associated with online purchase of perfume; and 3) identifies perfume e-shoppers' profiles. Design/Methodology/Approach: A qualitative research approach involving interviewing 27 perfume e-shoppers from Brazil, Iran and Portugal was adopted. Research findings: The findings of this study reveal that there are three types of perfume e-shoppers: 1) experienced e-shoppers, 2) bargain e-seekers, and 3) expert e-shoppers. Competitive prices, enjoyable online experiences and greater varieties of perfumes are among the perceived benefits that motivate the online shoppers. The inability to test the perfumes, particularly those that are never tried before, appears to be the major drawback. Theoretical contribution/Originality: This study provides interesting cues on a topic that has so far been disregarded by the literature despite its growing importance as a business sector-online purchase of perfumes. It offers the categorisation of three e-shopper profiles, based on consumers' narratives, and highlighted the importance of further addressing the dynamic nature of consumer behaviours, particularly in the online context. Practitioner/Policy implications: The findings show that the main trigger of buying perfume online is the price discounts, which is particularly important to less experienced Web shoppers. This indicates that experience goods such as perfume should, at the initial stage, be sold in stores that offer an assortment of products. Online perfume stores should consider alternatives to provide perfume testing. Research limitation/Implications: Purposive sampling does not allow results' generalisation; hence future research is needed to further validate the findings.

Abstract
This study aimed to identify dietary patterns (DPs) and their associations with sociodemographic factors and diet quality in Portuguese adults and the elderly. Cross-sectional data were obtained from the National Food, Nutrition and Physical Activity Survey (2015-2016), with two non-consecutive dietary 24 h recalls. Food items were classified according to the NOVA system and its proportion (in grams) in the total daily diet was considered to identify DPs by latent class analysis, using age and sex as concomitant variables. Multinomial logistic and linear regressions were performed to test associations of DPs with sociodemographic characteristics and diet quality, respectively. Three DPs were identified: "Traditional " (higher vegetables, fish, olive oil, breads, beer and wine intake), "Unhealthy " (higher pasta, sugar-sweetened beverages, confectionery and sausages intake) and "Diet concerns " (lower intake of cereals, red meat, sugar-sweetened and alcoholic beverages). "Unhealthy " was associated with being younger and lower intake of dietary fiber and vitamins and the highest free sugars and ultra-processed foods (UPF). "Diet concerns " was associated with being female and a more favorable nutrient profile, but both DPs presented a higher contribution of UPF than the "Traditional " DP. These findings should be considered for the design of food-based interventions and public policies for these age groups in Portugal.

Abstract

Abstract
Several approaches with interesting results have been proposed over the years for robot grasp planning. However, the industry suffers from the lack of an intuitive and reliable system able to automatically estimate grasp poses while also allowing the integration of grasp information from the accumulated knowledge of the end user. In the presented paper it is proposed a non-object-agnostic grasping pipeline motivated by picking use cases from the aerospace industry. The planning system extends the functionality of the simulated annealing optimization algorithm for allowing its application within an industrial use case. Therefore, this paper addresses the first step of the design of a reconfigurable and modular grasping pipeline. The key idea is the creation of an intuitive and functional grasping framework for being used by factory floor operators according to the task demands. This software pipeline is capable of generating grasp solutions in an offline phase, and later on, in the robot operation phase, can choose the best grasp pose by taking into consideration a set of heuristics that try to achieve a successful grasp while also requiring the least effort for the robotic arm. The results are presented in a simulated and a real factory environment, relying on a mobile platform developed for intralogistic tasks. With this architecture, new state-of-art methodologies can be integrated in the future for growing the grasping pipeline and make it more robust and applicable to a wider range of use cases.