Abstract
Human oversight can benefit scenarios with complex tasks, such as pallet docking and loading and unloading containers, beyond the current capabilities of autonomous systems without any failures. Furthermore, teleoperation systems allow remote control of mobile ground robots, especially with the surge of 5G technology that promises reliable and low latency communication. Current works research on exploring the latest features from the 5G standard, including ultra-Reliable Low-Latency Communication (uRLLC) and network slicing. However, these features may not be available depending on the Internet Service Provider (ISP) and communication devices. Thus, this work proposes a network architecture for the teleoperation of ground mobile robots in industrial environments using commercially available devices over the 5G Non-Standalone (NSA) standard. Experimental results include an evaluation of the network and End-to-End (E2E) latency of the proposed system. The results show that the proposed architecture enables teleoperation, achieving an average E2E latency of 347.19 ms. © 2024 IEEE.

Abstract
In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. This study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. The performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. The top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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.