Modeling of the aorta artery aneurysms and renal artery stenosis using cardiovascular electronic system

<p>Abstract</p> <p>Background</p> <p>The aortic aneurysm is a dilatation of the aortic wall which occurs in the saccular and fusiform types. The aortic aneurysms can rupture, if left untreated. The renal stenosis occurs when the flow of blood from the arteries leading to the kidneys is constricted by atherosclerotic plaque. This narrowing may lead to the renal failure. Previous works have shown that, modelling is a useful tool for understanding of cardiovascular system functioning and pathophysiology of the system. The present study is concerned with the modelling of aortic aneurysms and renal artery stenosis using the cardiovascular electronic system.</p> <p>Methods</p> <p>The geometrical models of the aortic aneurysms and renal artery stenosis, with different rates, were constructed based on the original anatomical data. The pressure drop of each section due to the aneurysms or stenosis was computed by means of computational fluid dynamics method. The compliance of each section with the aneurysms or stenosis is also calculated using the mathematical method. An electrical system representing the cardiovascular circulation was used to study the effects of these pressure drops and the compliance variations on this system.</p> <p>Results</p> <p>The results showed the decreasing of pressure along the aorta and renal arteries lengths, due to the aneurysms and stenosis, at the peak systole. The mathematical method demonstrated that compliances of the aorta sections and renal increased with the expansion rate of the aneurysms and stenosis. The results of the modelling, such as electrical pressure graphs, exhibited the features of the pathologies such as hypertension and were compared with the relevant experimental data.</p> <p>Conclusion</p> <p>We conclude from the study that the aortic aneurysms as well as renal artery stenosis may be the most important determinant of the arteries rupture and failure. Furthermore, these pathologies play important rules in increase of the cardiovascular pulse pressure which leads to the hypertension.</p

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks

Feature learning, i.e. extracting meaningful representations of data, is quintessential to the practical success of neural networks trained with gradient descent, yet it is notoriously difficult to explain how and why it occurs. Recent theoretical studies have shown that shallow neural networks optimized on a single task with gradient-based methods can learn meaningful features, extending our understanding beyond the neural tangent kernel or random feature regime in which negligible feature learning occurs. But in practice, neural networks are increasingly often trained on {\em many} tasks simultaneously with differing loss functions, and these prior analyses do not generalize to such settings. In the multi-task learning setting, a variety of studies have shown effective feature learning by simple linear models. However, multi-task learning via {\em nonlinear} models, arguably the most common learning paradigm in practice, remains largely mysterious. In this work, we present the first results proving feature learning occurs in a multi-task setting with a nonlinear model. We show that when the tasks are binary classification problems with labels depending on only $r$ directions within the ambient $d\gg r$-dimensional input space, executing a simple gradient-based multitask learning algorithm on a two-layer ReLU neural network learns the ground-truth $r$ directions. In particular, any downstream task on the $r$ ground-truth coordinates can be solved by learning a linear classifier with sample and neuron complexity independent of the ambient dimension $d$, while a random feature model requires exponential complexity in $d$ for such a guarantee

Feasibility Study on Discrimination of Neo-plastic and Non-Neoplastic Gastric Tissues Using Spark Discharge Assisted Laser Induced Breakdown Spectroscopy

Introduction: The present work is a novel in vitro study that evaluated the possibility of diagnosing neoplastic from nonneoplastic gastric tissues using spark discharge assisted laser induced breakdown spectroscopy (SD-LIBS) method.Methods: In these experiments, the low energy laser pulses ablated a tiny amount of tissue surface leading to plasma formation. Then, a spark discharge was applied to plasma in order to intensify the plasma radiation. Light emission from plasma was recorded as spectra which were analyzed. Gastric tissues of 5 people were studied through this method.Results: The SD-LIBS technique had the potential to discriminate normal and cancerous tissues based on the significant differences in the intensities of some particular elements. The comparison of normalized calcium (Ca) and magnesium (Mg) peaks of neoplastic and nonneoplastic gastric tissues could be viewed as a practical measure for tissue discrimination since Ca and Mg peaks in spectra of neoplastic were noticeably higher than nonneoplastic.Conclusion: Considering the identification of gastric cancer, the applied method in these experiments seems quite fast, noninvasive and cost-effective with respect to other conventional methods. The significant increment of specific Ca and Mg lines of neoplastic gastric tissues in comparison to the nonneoplastic ones can be considered as valuable information that might bring about tissue classification. The number of samples in this work, however, was not sufficient for a decisive conclusion and further researches is needed to generalize this idea

A Survey on Process Modelling, Innovation, Design, and Material Characterisation in Additive Manufacturing

The unique design freedom offered by additive manufacturing (AM) technologies enables engineers to develop more innovative products with relatively lower costs within a shorter period of processing time in comparison with conventional manufacturing methods. On the other hand, the unique capabilities of AM have created a platform for researchers to combine several engineering methods with the new manufacturing technique to grow industrial applications as well as resolve the existing issues with AM processes. Understanding the research values that AM offers academic environments, this paper performs a systematic survey on AM-related research topics in the fields of mechanical engineering and materials science that have attracted much attention from research teams over the last few years. These topics, namely process modelling in AM, innovative research in AM, generative design by AM, material characterisation in AM processes, and finally, design for additive manufacturing (DfAM), are notably investigated through this study