Abstract
The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

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Abstract
The advancement of technology has led to a growing demand for autonomy across various sectors. A key aspect of achieving autonomous navigation through intricate environments is path planning, initially confined to 2D spaces but rapidly evolving to address the complexities of 3D environments. Despite the widespread adoption of RRT-based planners, their inherent lack of optimality has encouraged researchers to find refinements. This paper transposes an existing algorithm developed for 2D environments to 3D, leveraging a heuristic to optimize the generated paths in terms of path length, memory consumed, and execution time. Along with this scalability to 3D scenarios, a modification was introduced that trades off some execution time for a substantial improvement in path length. The results obtained from a series of simulated experimental tests prove the efficacy of the proposed method in 3D environments, demonstrating reduced memory consumption and execution time compared to conventional approaches.

Abstract
X, previously known as Twitter, boasts 556 million active users and is widely used by businesses to engage with their audiences. In our study, we focused on TV Globo's telenovela "Terra e Paixão" broadcast in 2023, to analyze the impact of textual patterns on post virality using natural language processing techniques. Techniques like sentiment analysis, Part-Of-Speech Tagging, reinforcement scoring, TF-IDF, semantic similarity, and cosine similarity were utilized to identify attributes that contribute to a post's success, aiming to enhance marketing strategies. We employed language models like BERT, RoBERTa, and e5 in our analysis. Our findings indicate that while various metrics affect post engagement, the challenge remains complex. Textual characteristics, although essential, do not fully explain a publication's popularity, underscoring the need for a multifaceted approach to understanding social media dynamics. © 2024, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Pupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing opensource methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science TM, ScienceDirect (R), and Scopus (R) databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN) 1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.

Abstract
Background: The development of ulcers in the plantar region of the diabetic foot originates mainly from sites subjected to high pressure. The monitoring of these events using maximum allowable pressure thresholds is a fundamental procedure in the prevention of ulceration and its recurrence. Objective: The aim of this review was to identify data in the literature that reveal an objective threshold of plantar pressure in the diabetic foot, where pressure is classified as promoting ulceration. The aim is not to determine the best and only pressure threshold for ulceration, but rather to clarify the threshold values most used in clinical practice and research, also considering the devices used and possible applications for offloading plantar pressure. Design: A systematic review. Methods: The search was performed in three electronic databases, by the PRISMA methodology, for studies that used a pressure threshold to minimize the risk of ulceration in the diabetic foot. The selected studies were subjected to eligibility criteria. Results: Twenty-six studies were included in this review. Seven thresholds were identified, five of which are intended for the inside of the shoe: a threshold of average peak pressure of 200 kPa; 25 % and 40-80 % reduction from initial baseline pressure; 32-35 mm Hg for a capillary perfusion pressure; and a matrix of thresholds based on patient risk, shoe size and foot region. Two other thresholds are intended for the barefoot, 450 and 750 kPa. The threshold of 200 kPa of pressure inside the shoe is the most agreed upon among the studies. Regarding the prevention of ulceration and its recurrence, the efficacy of the proposed threshold matrix and the threshold of reducing baseline pressure by 40-80 % has not yet been evaluated, and the evidence for the remaining thresholds still needs further studies. Conclusions: Some heterogeneity was found in the studies, especially regarding the measurement systems used, the number of regions of interest and the number of steps to be considered for the threshold. Even so, this review reveals the way forward to obtain a threshold indicative of an effective steppingstone in the prevention of diabetic foot ulcer.