Abstract
Choreographic languages describe possible sequences of interactions among a set of agents. Typical models are based on languages or automata over sending and receiving actions. Pomsets provide a more compact alternative by using a partial order to explicitly represent causality and concurrency between these actions. However, pomsets offer no representation of choices, thus a set of pomsets is required to represent branching behaviour. For example, if an agent Alice can send one of two possible messages to Bob three times, one would need a set of 2 x 2 x 2 distinct pomsets to represent all possible branches of Alice's behaviour. This paper proposes an extension of pomsets, named branching pomsets, with a branching structure that can represent Alice's behaviour using 2 + 2 + 2 ordered actions. We compare the expressiveness of branching pomsets with that of several forms of event structures from the literature. We encode choreographies as branching pomsets and show that the pomset semantics of the encoded choreographies are bisimilar to their operational semantics. Furthermore, we define well-formedness conditions on branching pomsets, inspired by multiparty session types, and we prove that the well-formedness of a branching pomset is a sufficient condition for the realisability of the represented com-munication protocol. Finally, we present a prototype tool that implements our theory of branching pomsets, focusing on its applications to choreographies. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

Abstract

Abstract
This work presents a specialized tabu search algorithm applied to the problem of electric power distribution systems primary feeders' reconfiguration. The specialization is related to two fundamental aspects of the tabu search algorithm. The first proposal eliminates the concept of a list of prohibited attributes and the aspiration criterion, but also avoids the possibility of revisiting a candidate solution so that cycling is avoided by maintaining a tabu list with all previously visited solutions. The second proposal is the possibility of restarting the search from the incumbent solution while avoiding paths that can be formed by revisiting candidate solutions. A new strategy based on Prim's algorithm generates a high-quality initial solution for the problem. Tests are conducted using the 33-, 84-, 118-, 136-, and 415-node test systems. The results demonstrate the effectiveness of the proposal for solving the reconfiguration problem since the best-known solution for each system is achieved within highly efficient execution times.

Abstract
Numerous studies in waste management propose solutions to the Waste Collection Problem, often focusing on constraints such as time windows and truck capacity. Travel times between points play a vital role in optimizing waste collection. However, the methods for determining them are frequently omitted. Another parameter that has a great influence on waste collection is the time window. Here, the impact of time windows and travel times on the capacitated waste collection problem with time windows solution was assessed for collecting three waste types. Surprisingly, travel times were found to have minimal influence on route optimization, while time windows significantly affected the algorithm's ability to identify the most efficient collection route. Addressing these considerations is crucial for practical application and improving the performance of waste collection algorithms in real-world contexts.

Abstract
The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

Abstract
The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, respectively, 77.29% and 84.67%. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.