Geometrical optimization of a short-stem hip implant for the reduction of proximal stress shielding (ottimizzazione geometrica di uno stelo corto di protesi d?anca per la riduzione della schermatura del carico prossimale)

Nowadays the number of hip joints arthroplasty operations continues to increase because the elderly population is growing. Moreover, the global life expectancy is increasing and people adopt a more active way of life. For this reasons, the demand of implant revision operations is becoming more frequent. The operation procedure includes the surgical removal of the old implant and its substitution with a new one. Every time a new implant is inserted, it generates an alteration in the internal femur strain distribution, jeopardizing the remodeling process with the possibility of bone tissue loss. This is of major concern, particularly in the proximal Gruen zones, which are considered critical for implant stability and longevity. Today, different implant designs exist in the market; however there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. The aim of the study is to investigate the stress shielding effect generated by different implant design parameters on proximal femur, evaluating which ranges of those parameters lead to the most physiological conditions.

Catalytic reduction of organic pollutants using supported metal nanoparticles

Metal nanoparticle catalysts have in the last decades been extensively researched for their enhanced performance compared to their bulk counterpart. Properties of nanoparticles can be controlled by modifying their size and shape as well as adding a support and stabilizing agent. In this study, preformed colloidal gold nanoparticles supported on activated carbon were tested on the reduction of 4-nitrophenol by NaBH4, a model reaction for evaluating catalytic activity of metal nanoparticles and one with high significance in the remediation of industrial wastewaters. Methods of wastewater remediation are reviewed, with case studies from literature on two major reactions, ozonation and reduction, displaying the synergistic effects observed with bimetallic and trimetallic catalysts, as well as the effects of differences in metal and support. Several methods of preparation of nanoparticles are discussed, in particular, the sol immobilization technique, which was used to prepare the supported nanoparticles in this study. Different characterization techniques used in this study to evaluate the materials and spectroscopic techniques to analyze catalytic activities of the catalyst are reviewed: ultraviolet-visible (UV-Vis) spectroscopy, dynamic light scattering (DLS) analysis, X-ray diffraction (XRD) analysis and transmission electron microscopy (TEM) imaging. Optimization of catalytic parameters was carried out through modifications in the reaction setup. The effects of the molar ratio of reactants, stirring, type and amount of stabilizing agent are explored. Another important factor of an effective catalyst is its reusability and long-term stability, which was examined with suggestions for further studies. Lastly, a biochar support was newly tested for its potential as a replacement for activated carbon.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.