Abstract
Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy.

Abstract
[No abstract available]

Abstract
Digital images from orbital platforms are the main source of information for mapping and decision-making. Their use has become increasingly popular over the years and has expanded into various areas. Feature extraction in digital images has been widely researched in Image Analysis, Photogrammetry, and Computer Vision. Works related to feature extraction for the generation and updating of GISs are generally divided into anthropic features such as buildings and/or highways and natural features such as vegetation areas or bodies of water. One attractive methodology for feature extraction, especially for rivers and bodies of water, is based on active contours, formulated based on the evolution of curves, which can have parametric models (Snakes) or geometric models (Level set). In this context, this work intends to identify and compare some characteristics of parametric and geometric active contour methods and apply them to orbital images from the OLI and PAN sensors of the LANDSAT 8 and CBERS 4 satellites for feature extraction, correlating these characteristics with the parameters required in the mathematical models of active contours. The present work makes use of Digital Image Processing (DIP) methods, with the first processing stage known as pre-processing, consisting of interconnected tasks that can be used to extract some information about the objects present in the scene. Subsequently, in the processing stage, the features of interest are extracted with the help of the Fiji and Icy software using Level Set and Snake, respectively. Regardless of the method used, the results presented in this work show an extraction time compatible with application needs, as they are developed semi-automatically.

Abstract
Warehouses and distribution centres play a key role in any Supply Chain, particularly in the retail sector, where a network of stores needs to be replenished in a highly dynamic and increasingly uncertain context. In this regard, companies need to improve their intralogistics systems daily to ensure long-term competitiveness and sustainable growth. This is especially true in picking-by-line systems where many time-consuming and manual tasks are usually involved. This study introduces a new decision support tool based on simulation methods to aid the decision-making process in a picking-by-Line system, aimed to improve the overall picking operations efficiency, through human-centric perspective. A Discrete-Event-Simulation model is proposed to assess a set of parameters under several scenarios, driving a more informed decision-making process towards cost-effective strategies. The proposed approach was validated through an empirical case study showing its effectiveness in assisting operational planning decisions related to capacity and resource allocation. The system demonstrates promising versatility for application across varied warehouse environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
There is evidence that some outdoor events may have contributed to the spread of COVID-19. We updated an empirical methodology based on regression modeling and hypothesis testing to analyze the potential impact of a demonstration that took place in Lisbon, within the scope of the ’Black Lives Matter’ context, on the contagion pattern in the region where this event occurred. We find that in the post-impact period there was no acceleration in the number of cases in the region, unlike in a prior event in the region. The proportion of counties where there was a potential impact of the event is not statistically significant. This result demonstrates that not all outdoor events contributed to the spread of COVID-19 and exemplifies how to apply the selected empirical methodology.

Abstract
This paper addresses the problem of high-bandwidth communication and data recovery from deep-sea semi-permanent robotic landers. These vehicles are suitable for long-term monitoring of underwater activities and to support the operation of other robotic assets in Operation & Maintenance (O&M) of offshore renewables. Limitations of current communication solutions underwater deny the immediate transmission of the collected data to the surface, which is alternatively stored locally inside each lander. Therefore, data recovery often implies the interruption of the designated tasks so that the vehicle can return to the surface and transmit the collected data. Resorting to a short-range and high-bandwidth optical link, an alternative underwater strategy for flexible data exchange is presented. It involves the usage of an AUV satellite approaching each underwater node until an optical communication channel is established. At this point, high-bandwidth communication with the remote lander becomes available, offering the possibility to perform a variety of operations, including the download of previously recorded information, the visualisation of video streams from the lander on-board cameras, or even performing remote motion control of the lander. All these three operations were tested and validated with the experimental setup reported here. The experiments were performed in the Atlantic Ocean, at Setubal underwater canyon, reaching the operation depth of 350m meters. Two autonomous robotic platforms were used in the experiments, namely the TURTLE3 lander and the EVA Hybrid Autonomous Underwater Vehicle. Since EVA kept a tether fibre optic connection to the Mar Profundo support vessel, it was possible to establish a full communication chain between a landbased control centre and the remote underwater nodes.