The application of Shape Memory Alloys with superelastic behavior (SMA–SE) to reduce vibrations in rotor systems has been studied in recent years. However, the current SMA damper device design does not prioritize improving the distribution of applied force due to vibration levels. In this sense, this paper aims to study (theoretical and experimentally) two different types of passive damper devices made of a Ni-Ti SMA–SE to be incorporated in a rotor system in order to reduce mechanical vibrations. Two different physical situations were analyzed in two new designs (A and B) of SMA–SE bending springs. In order to obtain parameters similar to real conditions for the displacement and cycle routines of simulation, experimental tests were made with an unbalanced rotor system first, where the maximum displacement was used for simulations as extreme conditions for this kind of system. The first study was the application of loads and releases, the second is related to applications of preloads and peak-to-peak displacement variations with the aim of verifying real conditions. From the obtained results, it was verified that the damper design of device A reached a better performance (5.63% of specific damping capacity) than that of device B (2.65% of specific damping capacity) during the martensitic transformation, and reaching the full martensitic transformation with less displacement applied for the first setup. For the second setup, the device A reached 12.63% and device B reached 6.44% of the specific damping capacity. Thus, the bending springs of type A are more efficient in reducing mechanical vibration using the damping properties of the SMA-SE.

In the forward flight, wind loads affect the helicopters and cause vibration. This paper analyzes the behavior of a helicopter prototype composed by two blades when subjected to a front wind load, similar to the forwarding flight condition. An Artificial Neural Network (ANN) processes the experimental data in order to identify the pattern of its dynamic behavior. The tests led to Vibration analysis for different wind speeds. Also, the data indicates that vibration amplitude increases when the blades are subjected to the fundamental frequency and its first harmonic on tests conducted without rotor plane tilt (hover flight). On the other hand, the second test performs a 5-degree tilt on the rotor disc. In this test, the vibration amplitude decreased in the fundamental frequency, and the amplitude related to the first harmonic increased. The ANN achieved 100% efficiency in recognizing the flight conditions of the prototype.

The mitral valve is the most susceptible to pathological alterations, such as mitral stenosis, characterized by failure of the valve to open completely. In this context, the objective of this study was to apply digital image processing (DIP) and develop a convolutional neural network (CNN) to provide decision support for specialists in the diagnosis of mitral stenosis based on transesophageal echocardiography examinations. The following procedures were implemented: acquisition of echocardiogram exams; application of DIP; use of augmentation techniques; and development of a CNN. The DIP classified 26.7% cases without stenosis, 26.7% with mild stenosis, 13.3% with moderate stenosis, and 33.3% with severe stenosis. A CNN was initially developed to classify videos into those four categories. However, the number of acquired exams was insufficient to effectively train the model for this purpose. So, the final model was trained to differentiate between videos with or without stenosis, achieving an accuracy of 92% with a loss of 0.26. The results demonstrate that both DIP and CNN are effective in distinguishing between cases with and without stenosis. Moreover, DIP was capable of classifying varying degrees of stenosis severity—mild, moderate, and severe—highlighting its potential as a valuable tool in clinical decision support.

Fundamento A avaliação da área valvar mitral por meio da reconstrução multiplano na ecocardiografia tridimensional é restrita a softwares específicos e à experiência dos ecocardiografistas. Eles precisam selecionar manualmente o frame do vídeo que contenha a área de abertura máxima da valva mitral, dimensão fundamental para a identificação de estenose mitral. Objetivo Automatizar o processo de determinação da área de abertura máxima da valva mitral, por meio da aplicação de Processamento Digital de Imagens (PDI) em exames de ecocardiograma, desenvolvendo um algoritmo aberto com leitura de vídeo no formato avi. Método Este estudo piloto observacional transversal foi realizado com vinte e cinco exames diferentes de ecocardiograma, sendo quinze com abertura normal e dez com estenose mitral reumática. Todos os exames foram realizados e disponibilizados por dois especialistas, com autorização do Comitê de Ética em Pesquisa, que utilizaram dois modelos de aparelhos ecocardiográficos: Vivid E95 (GE Healthcare) e Epiq 7 (Philips), com sondas multiplanares transesofágicas. Todos os vídeos em formato avi foram submetidos ao PDI através da técnica de segmentação de imagens. Resultados As medidas obtidas manualmente por ecocardiografistas experientes e os valores calculados pelo sistema desenvolvido foram comparados utilizando o diagrama de Bland-Altman. Observou-se maior concordância entre valores no intervalo de 0,4 a 2,7 cm2. Conclusão Foi possível determinar automaticamente a área de máxima abertura das valvas mitrais, tanto para os casos advindos da GE quanto da Philips, utilizando apenas um vídeo como dado de entrada. O algoritmo demonstrou economizar tempo nas medições quando comparado com a mensuração habitual.