Microstructure and mechanical properties of hard Acrocomia mexicana fruit shell.

Fruit and nut shells can exhibit high hardness and toughness. In the peninsula of Yucatan, Mexico, the fruit of the Cocoyol palm tree (Acrocomia mexicana) is well known to be very difficult to break. Its hardness has been documented since the 1500 s, and is even mentioned in the popular Maya legend The Dwarf of Uxmal. However, until now, no scientific studies quantifying the mechanical performance of the Cocoyol endocarp has been found in the literature to prove or disprove that this fruit shell is indeed "very hard". Here we report the mechanical properties, microstructure and hardness of this material. The mechanical measurements showed compressive strength values of up to ~150 and ~250 MPa under quasi-static and high strain rate loading conditions, respectively, and microhardness of up to ~0.36 GPa. Our findings reveal a complex hierarchical structure showing that the Cocoyol shell is a functionally graded material with distinctive layers along the radial directions. These findings demonstrate that structure-property relationships make this material hard and tough. The mechanical results and the microstructure presented herein encourage designing new types of bioinspired superior synthetic materials.

Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients.

This work deals with the assessment of different parameterization techniques for lung sounds (LS) acquired on the whole posterior thoracic surface for normal versus abnormal LS classification. Besides the conventional technique of power spectral density (PSD), the eigenvalues of the covariance matrix and both the univariate autoregressive (UAR) and the multivariate autoregressive models (MAR) were applied for constructing feature vectors as input to a supervised neural network (SNN). The results showed the effectiveness of the UAR modeling for multichannel LS parameterization, using new data, with classification accuracy of 75% and 93% for healthy subjects and patients, respectively.

Base lung sound in diffuse interstitial pneumonia analyzed by linear and nonlinear techniques.

Abnormal lung sounds in diffuse interstitial pneumonia have been characterized by the presence of crackles. However, few attempts have tried to analyze the sound where crackles are immersed. In this work base lung sounds (BLS) were analyzed by linear and nonlinear techniques to find possible differences between normal subjects and patients with diffuse interstitial pneumonia. In both groups, segments of lung sounds (crackles absent) and segments of BLS (lung sound in between crackles) were analyzed from acquired lung sounds from four points at the posterior chest, two apical and two basal. In this study, 8 healthy subjects and 8 patients participated and BLS were analyzed by spectral percentiles and sample entropy. Although spectral percentiles and sample entropy (SampEn) tended to be lower in the group of patients, statistical differences (p0.05) between normal subjects and patients were found in BLS at the left hemithorax at basal and apical regions, while at the right hemithorax significant differences were found only at the basal region using the nonlinear technique. We conclude that in respect to normal subjects, BLS of patients with diffuse interstitial pneumonia present differences as assessed by SampEn, so that BLS by itself provides useful information. Moreover, it seems that nonlinear technique did a better discrimination of abnormal BLS than spectral percentile parameters.

Crackles detection using a time-variant autoregressive model.

Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis. The coefficients of the TVAR were obtained by an adaptive filtering prediction scheme. The adaptive filter used the recursive least squares algorithm with a forgetting factor of 0.97 and the model order was four. TVAR model showed an efficiency to detect crackles over 90% even with crackles overlapping and amplitudes as low as 1.5 of the standard deviation of background lung sounds, where expert presented an efficiency around 30%. In conclusion, TVAR model is a proper alternative to detect and to provide an estimate number of fine and coarse crackles, even in presence of crackles overlapping and crackles with low amplitude, conditions where crackles detection based on time-expanded waveform-analysis reveals evident limitations.