Abstract
Solar energy is a compelling and growing avenue for transitioning towards reliance on sustainable and environmentally friendly energy sources. However, the performance of individual photovoltaic (PV) panels is vulnerable to defects inflicted by weather exposure. The industry currently addresses these challenges by conducting manual inspections at each PV installation, a process that is highly time-consuming, financially burdensome, difficult to scale, prone to human error, and sometimes unfeasible due to topographical constraints. This study aims to develop a solution that detects these defects in real time resorting to an Autonomous Aerial Vehicle (AAV) equipped with for a thermal and a visual sensor. This paper contributes three major advancements to PV defect detection: a robust, standardized dataset built following IEC TS 62446-3 specifications with annotations from a real PV power plant, a novel multi-spectral approach that leverages early fusion of visual and thermal data captured via an AAV, and benchmark performance metrics for defect detection using state-of-the-art AI models. Our implementation uses Real-Time DEtection TRansformer (RT-DETR) and You Only Look Once (YOLO) models, achieving a mean Average Precision@50 (mAP) of 94% for RT-DETR and a mAP@50 of nearly 95% for YOLOv11. These results demonstrate the effectiveness of our approach in real-world settings, addressing a significant operational challenge in PV plant maintenance while establishing new performance benchmarks for automated defect detection in industrial solar installations.

Abstract
Algorithms based on computer vision are commonly used in pre-operative procedures to help health professionals detecting blood vessels, which is also the case with the Deep Inferior Epigastric Perforators (DIEPs). These blood vessels are essential to produce a viable autologous DIEP flap, and the analysis of characteristics such as their location, diameter and course is essential to ensure the success of surgeries. This analysis is made by a team of radiology technicians and then validated by a surgeon, making it a complex process that can take up to 2 hours. The proposed algorithm called Assisted Vascular Analysis (AVA) was developed to ensure a faster alternative to the conventional methods, using automation to identify structures of interest such as the skin, umbilicus and fascia, while also requiring minimum input from the user to segment each DIEP (2 points for the subcutaneous portion and 2 for the intramuscular portion). The AVA feasibility tests where conducted using 6 Computed Tomography Angiographies (CTAs), with a total of 28 DIEP calibers obtained during surgery (ground truths) from patients that underwent breast reconstruction with a DIEP flap. The algorithm was evaluated for its capability to segment the DIEPs and measure their caliber, comparing the results with the ground truth calibers and the manual mapping done by the radiology technicians. The Root Mean Square Error (RMSE) metric shows that the calibers obtained by the AVA algorithm (0.57 millimeters) and the radiology technicians (0.46 millimeters) are very similar, with the radiology technicians gaining a smaller edge of 0.11 millimeters. These results are very promising, since the errors are inferior to the average image resolution (0.88 millimeters). It was also demonstrated that the AVA algorithm is a faster alternative to manual segmentation, taking around 10 minutes to fully analyze each CTA, while the radiology technicians takes around 1 hour to do the DIEP mapping and caliber measurements. In conclusion, AVA is a validated algorithm to segment DIEP vessels and a faster alternative compared with conventional methods. © 2025 IEEE.

Abstract
This study presents the development and assessment of a mobile application - the WalkingPAD app - aimed at promoting adherence to physical exercise among patients with Peripheral Arterial Disease (PAD). The assessment of adherence, acceptability, and usability was performed using mixed methods. Thirty-eight patients participated in the study with a mean age of 63.4 years (SD = 6.8). Thirty patients used the application for three months, responded to a semi-structured interview, and completed a task test and the System Usability Scale (SUS, ranging from 0 to 100). The application's adherence rate was 73%. When patients were asked about their reasons for using the app, the main themes that emerged were motivation, self-monitoring, and support in fulfilling a commitment. The average SUS score was 82.82 (SD = 18.4), indicating high usability. An upcoming version of the WalkingPAD app is expected to redesign both tasks - opening the app and looking up the walking history - which were rated as the most difficult tasks to accomplish. The new version of the WalkingPAD app will incorporate participants' comments and suggestions to enhance usability for this population.

Abstract
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark (i) the proposal of novel Generative AI methods and synthetic data, and (ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace.

Abstract
Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. © The Author(s) 2025.

Abstract