Le prince Sabaheddine:Son activite politique en Turquie

Taha Toros Arşivi, Dosya Adı: Prens Sabahattinİstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

Predicting Geometric Errors and Failures in Additive Manufacturing

Additive manufacturing is a process that has facilitated the cost effective production of complicated designs. Objects fabricated via additive manufacturing technologies often suffer from dimensional accuracy issues and other part specific problems such as thin part robustness, overhang geometries that may collapse, support structures that cannot be removed, engraved and embossed details that are indistinguishable. In this work we present an approach to predict the dimensional accuracy per vertex and per part. Furthermore, we provide a framework for estimating the probability that a model is fabricated correctly via an additive manufacturing technology for a specific application. This framework can be applied to several 3D printing technologies and applications. In the context of this paper, a thorough experimental evaluation is presented for binder jetting technology and applications.Comment: This version has been published in the Rapid Prototyping Journal (2023

Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

<p>Abstract</p> <p>Background</p> <p>Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the <it>S. cerevisiae </it>interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.</p> <p>Results</p> <p>We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.</p> <p>Conclusions</p> <p>The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.</p

Superclusteroid: a Web tool dedicated to data processing of protein-protein interaction networks

The study of proteins and the interactions between them, known as Protein-Protein Interactions (PPI), is extremely important in interpreting all biological cellular functions. In this article, a new web tool called Superclusteroid is presented which can analyse PPI data, in order to detect protein complexes or characterise the functionality of unknown proteins. The tool is essentially an intuitive PPI data processing pipeline. It supports various input file formats and provides services such as clustering, PPI network visualisation and protein cluster function prediction. Each Superclusteroid service can be used in a sequential manner or on an individual basis. In order to assess the reliability of our tool to infer PPIs, the results of the tool were compared to already known MIPS database complexes and a case scenario is presented where a known protein complex is predicted and the functionality of some of its proteins is revealed