Analytical model for the prediction of wrinkling of rear pressure bulkheads

In Airbus GmbH (Hamburg) has been developed a new design of Rear Pressure Bulkhead (RPB) for the A320-family. The new model has been formed with vacuum forming technology. During this process the wrinkling phenomenon occurs. In this thesis is described an analytical model for prediction of wrinkling based on the energetic method of Timoshenko. Large deflection theory has been used for analyze two cases of study: a simply supported circular thin plate stamped by a spherical punch and a simply supported circular thin plate formed with vacuum forming technique. If the edges are free to displace radially, thin plates will develop radial wrinkles near the edge at a central deflection approximately equal to four plate thicknesses w0/ℎ≈4 if they’re stamped by a spherical punch and w0/ℎ≈3 if they’re formed with vacuum forming technique. Initially, there are four symmetrical wrinkles, but the number increases if the central deflection is increased. By using experimental results, the “Snaptrhough” phenomenon is described.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

A membrane process to extract Citrus Lemon derived extracellular vesicles

Extra cellular vesicles are membrane bound and lipid based nano particles having the size range of 30 to 1000 nm released by a plethora of cells. Their prime function is cellular communication but in the recent studies, the potential of these vesicles to maintain physiological and pathological processes as well as their nano-sized constituents opened doors to its applications in therapeutics, and diagnostics of variety of diseases such as cancer. Their main constituents include lipids, proteins, and RNAs. They are categorized into subtypes such as exosomes, micro-vesicles and apoptotic bodies In recent studies, extracellular vesicles that are derived from plants are gaining high regard due to their variety of advantages such as safety, non-toxicity, and high availability which promotes large scale production. EVs are isolated from mammalian and plant cells using multitude of techniques such as Ultracentrifugation, SEC, Precipitation and so on. Due to the variety in the sources as well as shortcomings arising from the isolation method, a scalable and inexpensive EV isolation method is yet to be designed. This study focusses on isolation of EVs from citrus lemon juice through diafiltration. Lemon is a promising source due to its biological properties to act as antioxidant, anticancer, and anti-inflammatory agents. Lemon derived vesicles was proven to have several proteins analogous to mammalian vesicles. A diafiltration could be carried out for successful removal of impurities and it is a scalable, continuous technique with potentially lower process times. The concentration of purified product and impurities are analysed using Size Exclusion Chromatography in analytical mode. It is also considered imperative to compare the results from diafiltration with gold standard UC. BCA is proposed to evaluate total protein content and DLS for size measurements. Finally, the ideal mode of storage of EVs to protect its internals and its structure is analysed with storage tests.