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Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images. [ABSTRACT FROM AUTHOR]

Electromagnetic forces can lead to the structural failure of power transformers due to extreme loading conditions, vibration, and/or fatigue. Therefore, studying the nature and the magnitude of these forces is a key task in the design and failure analysis of such important equipment. Keeping this issue in mind, this work aims at conducting a numerical analysis in order to evaluate the mechanical stresses and displacements of windings in power transformers due to the action of electromagnetic forces. With this purpose, a finite element model is developed considering electromagnetic and mechanical effects assuming short-circuit conditions. The study compares the cases employing copper and aluminum windings with header tap, in different temperatures. The model developed in this work is verified against analytical solutions. The numerical calculations allow for performing a detailed analysis in terms of the distribution of both displacements and stresses along the windings, which is of great relevance for identifying critical structural points and avoiding structural failure. Overall, the obtained results demonstrate that the finite element model is a useful tool for the structural design of power transformers that allow for investigating and optimizing key aspects before manufacturing. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain's harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated. [ABSTRACT FROM AUTHOR]

The study aimed to estimate the prevalence of skin problems in children and adolescents with type 1 diabetes (T1D) using insulin pumps (IPs) and/or continuous glucose monitoring (CGM) in our center and analyze their association with various factors. As part of the international ISPAD JENIOUS-initiated SKIN-PEDIC project, we interviewed and examined patients who visited the regional pediatric diabetes center in Opole (Poland) for four weeks regarding the use of IP and/or CGM and the presence of skin problems. Body mass index (BMI) and glycemic parameters were obtained retrospectively from medical records. Among 115 individuals (45.2% girls, 83.5% IP users, 96.5% CGM users), old scars were the most common skin problem (IP users 53.1%; CGM users 66.4%), while ≥2 types of skin problems co-occurred (IP users 40.6%; CGM users 27.3%). Longer IP use was associated with a higher prevalence of skin problems (50% for IP < 1 year, 98.1%-IP 1–3 years, 100% for IP > 3 years; p < 0.001), pointing out extra attention with IP use > 1 year. No significant associations were found between skin problems and gender, age, BMI centile and glycemic parameters. Dermatological complications were common among children using IP and CGM in our center, highlighting the need for vigilant monitoring and early intervention to manage these skin-related issues effectively. [ABSTRACT FROM AUTHOR]