Unsupervised pattern-recognition and radiological risk assessment applied to the evaluation of behavior of rare earth elements, Th, and U in monazite sand.

The Brazilian coast is rich in monazite which is found in beach sand deposits. In this study, the composition of the monazite sands from beaches of State of Espírito Santo, Brazil, was investigated. The concentrations of rare earth elements (REEs), Th, and U were determined by inductively coupled plasma mass spectrometry (ICP-MS). In the studied region, the mean concentration of investigated elements increased in the following order: Tm < Yb < Ho < Lu < Eu < Er < Tb < Dy < U < Y < Th < Gd < Sm < Pr < Nd < La < Ce. The sampling sites were classified into three clusters and discriminated by the concentrations of REEs, Th, and U found. In general, the radiological risk indices were higher than the established limits, and the risk of developing cancer was estimated to be higher than the world average.

CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images.

The early detection of breast cancer is a vital factor when it comes to improving cure and recovery rates in patients. Among such early detection factors, one finds thermography, an imaging technique that demonstrates good potential as an early detection method. Convolutional neural networks (CNNs) are widely used in image classification tasks, but finding good hyperparameters and architectures for these is not a simple task. In this study, we use two bio-inspired optimization techniques, genetic algorithm and particle swarm optimization to find good hyperparameters and architectures for the fully connected layers of three state of the art CNNs: VGG-16, ResNet-50 and DenseNet-201. Through use of optimization techniques, we obtained F1-score results above 0.90 for all three networks, an improvement from 0.66 of the F1-score to 0.92 of the F1-score for the VGG-16. Moreover, we were also able to improve the ResNet-50 from 0.83 of the F1-score to 0.90 of the F1-score for the test data, when compared to previously published studies.

The influence of beach geology and morphodynamics on chemical pollution assessments following a mining accident.

In the present study the distribution of chemical elements in beaches adjacent to the Doce River mouth hit by the tailings mud from a mining accident were assessed. Sedimentological and morphological coastal aspects were also considered. The results indicate that wave-exposed delta plain beaches exhibit high resiliency, despite their proximity to potential pollution sources. On the other hand, shore platform beaches tend to accumulate chemical elements, mainly due to limited cross-shore sediment exchanges. Arsenic concentrations in the evaluated shore platform beaches were significantly higher than the delta plain beach. Shore platform beaches are more susceptible to frequent flooding and to higher elemental concentrations at the berm and beach face. Thus, the morphological characteristics of the assessed shore platform beaches, and input from the mud plume must be considered in a joint assessment strategy in order to obtain a broad understanding of the actual scenario regarding beach contamination.

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence.

Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.

An evaluation of machine learning methods for speed-bump detection on a GoPro dataset.

Every day, new applications arise relying on the use of high-resolution road maps in both academic and industrial environments. Autonomous vehicles rely on digital maps to navigate when optical sensors cannot be trusted, such as heavy rainfalls, snowy conditions, fog, and other situations. These situations increase the risks of accidents and disable the potentials of real-time mapping sensors. To tackle those problems, we present a methodology to automatically map anomalies on the road, namely speed bumps in this study, using an off-the-shelf camera (GoPro) and Machine Learning (ML) algorithms. We acquired data over a series of differently shaped speed bumps and applied three classification techniques: Naive Bayes, Multi-Layer Perceptron, and Random Forest (RF). With over 96% of classification accuracy, then RF was able to identify speed bumps on a GoPro dataset automatically. The results show a potential of the proposed methodology to be developed in surveying vehicles to produce highly-detailed maps of vertical road anomalies with a fast and accurate update rate.