Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CNN) on 3D ultrasonic data, facilitated by the fusion of X-ray computed tomography (XCT) and Phased-Array Ultrasonic Testing (PAUT) data. This approach offers the ability to develop supervised datasets for cases where UT techniques inadequately assess defects and enables the creation of models with genuine defects rather than artificially introduced ones. During the training process, we recommend processing the 3D volumes as a sequence of 2D slices derived from each technique. Our methodology was applied to segment porosity, a common defect in composite materials, for which characteristics such as void size and shape remain immeasurable via UT. Precision, recall, F1 score, and Intersection over Union (IoU) metrics were used in the evaluation. The results of the evaluation show that the following challenges have to be faced for improvement: (i) achieving accurate 3D registration, (ii) discovering suitable similar keypoints for XCT and UT data registration, (iii) differentiating ultrasonic echoes originating from porosity versus those related to noise or microstructural features (interfaces, resin pockets, fibers, etc.), and, (iv) single out defect echoes located near the edges of the component. In fact, an average F1 score of 0.66 and IoU of 0.5 were obtained.

Mobile devices equipped with sensors are generating an amount of geo-spatial related data that, properly analyzed can be used for future applications. In particular, being able to establish similar trajectories is crucial to analyze events on common points in the trajectories. CROSS-CPP is a European project whose main aim is to provide tools to store data in a data market and to have a toolbox to analyze the data. As part of these analytic tools, a set of functionalities has been developed to cluster trajectories. Based on previous work on clustering algorithms we present in this paper a Quickbundels algorithm adaptation to trajectory clustering . Experiments using different distance measures show that Quickbundles outperforms spectral clustering, with the WS84 geodesic distance being the one that provides the best results.

Nowadays the aerospace and energetic industries use ultrasonic non destructive testing (NDT) to assess the presence of porosity in composite material parts. Current ultrasonic inspection techniques yield estimations of void fraction with not enough accuracy, which is relevant when high quality parts are required. Relevant factors such as the distribution, shape and size of porosity

Response in the growth of Oreochromis niloticus juveniles fed with two types of diet: Experimental at 28% protein (based on banana peel and soy), vs. Commercial at 28%. To determine which treatment obtained the highest growth, physical-chemical factors (Dissolved Oxygen, Temperature and pH) and population parameters (Accumulated Growth, Survival and Feed Conversion Factor) were taken. Collecting these data for 25 days where the Tilapia Oreochromis niloticus had an initial weight of 7.9 gr in both treatments. According to the results obtained in the experiment, the treatment with commercial feed at 28% protein acquired a final growth of 13.1 g, obtaining an average of 10.5 g, instead the treatment with experimental feed at 28% protein based on banana peel flour. and soybeans acquired a final weight of 10.2 gr, obtaining an average of 8.97 gr. The commercial treatment at 28% protein gained more size and biomass because they assimilated the food in a greater way and in less time, in contrast to the treatment with experimental food at 28% protein based on banana and soybean peel flour. gained little biomass and less size since they took time to assimilate and get used to the food that was put to experimental, when applying the statistical analysis it was found that p> 0.05 that the growth of the Tilapias Oreochromis niloticus there is a significant difference because between treatments There is a big difference between the weights.