The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Obesity is recognized as a major public health issue, as it is linked to the increased risk of severe pathological conditions. The aim of this pilot study is to evaluate the relations between adiposity (and biophysical characteristics) and temperature profiles under thermoneutral conditions in normal and overweight females, investigating the potential role of heat production/dissipation alteration in obesity. We used Infrared Thermography (IRT) to evaluate the thermogenic response to a metabolic stimulus performed with an oral glucose tolerance test (OGTT). Thermographic images of the right hand and of the central abdomen (regions of interests) were obtained basally and during the oral glucose tolerance test (3 h OGTT with the ingestion of 75 g of oral glucose) in normal and overweight females. Regional temperature vs BMI, % of body fat and abdominal skinfold were statistically compared between two groups. The study showed that mean abdominal temperature was significantly greater in lean than overweight participants (34.11 ± 0.70 °C compared with 32.92 ± 1.24 °C, p < 0.05). Mean hand temperature was significantly greater in overweight than lean subjects (31.87 ± 3.06 °C compared with 28.22 ± 3.11 °C, p < 0.05). We observed differences in temperature profiles during OGTT between lean and overweight subjects: The overweight individuals depict a flat response as compared to the physiological rise observed in lean individuals. This observed difference in thermal pattern suggests an energy rate imbalance towards nutrients storage of the overweight subjects.

The inspection of power lines is the crucial task for the safe operation of power transmission: its components require regular checking to detect damages and faults that are caused by corrosion or...

Marine debris poses a substantial threat to ecosystems and their inhabitants, representing a worldwide issue. It is crucial to understand the nature and extent of this environmental issue, given its widespread distribution and diverse nature. In recent years, aerial and underwater imaging has been extensively employed to monitor these debris. The classification of marine debris facilitates the identification of the specific types involved and is crucial for implementing preventive measures. Nevertheless, monitoring demands substantial human effort, indicating the need for automated and cost-effective methodologies. In this context, artificial intelligence has emerged as a powerful tool for identifying underwater marine debris, offering several advantages over conventional monitoring processes, such as manual visual surveys conducted by divers or the use of remotely operated vehicles. We evaluated deep architectures through transfer learning, utilizing knowledge derived from the ImageNet data set and applying it to the smaller data set, considering the limited availability of underwater marine data. While prior literature exclusively focused on convolutional neural network (CNN)-based architectures in their comparative study, this article additionally incorporates state-of-the-art attention-based deep architectures for classifying underwater marine debris. We utilized multiple training schemes for classifying marine debris, employing a data set sourced from the database of The Japan Agency for Marine-Earth Science and Technology, which comprises of a library of deep-sea images. The attention-based shifted window (Swin) architecture demonstrated the highest accuracy at 93.08% with relatively higher parameter count, followed by DenseNet121 with an accuracy of 90.99%. MobileNetV2, with the fewest parameters (2.26 M), achieved an accuracy of 84.91%. The comparative analysis in this study indicates that transformer-based models exhibited relatively higher accuracies compared to CNNs. However, the selection between CNNs and transformer-based architectures depends on multiple factors such as data availability, computational resources and marine environment.