Abstract

Abstract
The welfare and safety of the employees of an enterprise is a great concern and priority in a responsible and successful organization. The identification of patterns of work-related accidents is important to reduce and prevent further mishaps and injuries. To improve the safety of the work environment, accidents related data must be analyzed to identify the possible risk factors and their effects on the type of accident and its level of severity. Thus, data related to workplace accidents in fishmonger stores were collected from a Portuguese retail company where it was analyzed with statistical, clustering, and classification techniques to identify potential underlying correlation and patterns between the data, and in this way, collecting important information to prevent future accident or lesions.

Abstract
The tradition of wine production and consumption in Portugal is widely spread since the country presents climatic and territorial characteristics which have made wine-making an important strategic sector. In addition, the essence of the wine industry has led to greater tourism, thus enhancing the growth of enotourism. Given the importance of the wine production sector in the national context, as well as the potential of Industry 4.0 to stimulate improvements both in efficiency and competitiveness, the objective of this work is to achieve a better understanding of how Industry 4.0 and its key features, namely simulation, can influence wine production and enotourism. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
The global trend guided by the energy systems decarbonization, decentralization and digitalization combined with the increase of distributed Renewable Energy Sources (RES) are allowing prosumers to take a more active role in the electricity markets. In this context, a market structure based on Peer-to-Peer (P2P) transactions is very promising but presents challenges for the network's operation. A critical challenge is to ensure that network constraints are not violated during energy trade between peers. Thus, the main contribution of this paper is the development of a methodology for the optimization of P2P energy transactions, accounting for network operation. The paper proposes a three-step approach (P2PTDF), using Topological Distribution Factors (TDF) to penalize peers responsible for violations that may occur, ensuring a feasible solution. Simulations were performed with the modified IEEE 14-bus system with 19 peers, including the possibility of exchanging energy with an external grid.

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.