Holm Browning (saladhead6)

Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0-50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 (mAP@0.5) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples ( less then 15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.Knowledge of the forces applied to the pedals during cycling is of great importance both from the point of view of improving sporting performance and medical analysis of injuries. The most common equipment for measuring pedal forces is usually limited to the study of forces in the sagittal plane. Equipment that measures three-dimensional forces tends to be bulky and to be incorporated into bicycles that are modified to accommodate it, which can cause the measurements taken to differ from those obtained in real pedalling conditions. click here This work presents a device for measuring the 3D forces applied to the pedal, attachable to a conventional bicycle and pedals, which does not alter the natural pedalling of cyclists. The equipment consists of four gauges located on the pedal axis and two on the crank, controlled by a microcontroller. Pedal forces measurements were made for six cyclists, with results similar to those shown in the literature. The correct estimation of the lateral-medial direction force is of great interest when evaluating a possible overload at the joints; it will also allow a comparison of the effectiveness index during pedalling, showing the role of this component in this index from a mechanical standpoint.Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B's vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber-vulnerabilities. Finally, a set of simulations and field experiments is presente