Futur evolution of the digital fabrication laboratories. Practical application to Neàpolis

Treball desenvolupat en el marc del programa "European Project Semester".The paper reviews the study of the state of the art of Fabrication Laboratories in order to implement them on Neàpolis buildings. The Neàpolis Fabrication Laboratories Project is conducted by a team of 4 international students participating in the European Project Semester 2019 in cooperation with Neàpolis agency. There is a lack of existing platform that encourages the maker culture, innovative ideas and creative thinking skills in Vilanova I La Geltrù. Therefore, Neàpolis proposed a project to develop Fabrication Laboratories into their buildings to provide the mentioned platform. This concept is going to be implemented in the Neàpolis main building, the La Nau building from Neàpolis district and the sea container building. First of all, a researched on the general information about Fabrication Laboratories, the differences between Maker Space and the existing Fabrication Laboratories were carried out. Next, several proposals for the designs and implementation of the Fabrication Laboratories were done for each of the three buildings. Subsequently, a single final proposal was chosen for each building and a list of machines and equipment was put together for the Fabrication Laboratories. Afterwards, the constructions and installation processes needed was carefully analyzed. Over the whole planning period, the overall cost estimations of Fabrication Laboratories in each building was calculated to give an idea of the project budget. This project shows that the Fabrication Laboratories concept is applicable for Neàpolis and hence, will provide a suitable platform for development of innovative ideas. Besides that, different designs of Fabrication Laboratories in different places around the world would still serve the same purposes and functionalities. The hybridization concept could also be achieved along with this existing platform. Moreover, the said platform would make Vilanova I La Geltrú be a part of the knowledge-sharing community across the world. Therefore, it is hoped that this project will be physically carried out by Neàpolis in the future as there is a lot of positive outcomes of this project.Incomin

Junction-Producing Algorithm Connecting Carbon Nanotube to Carbon Nanocone to Obtain Funnel-Like Nanostructure: Nanochimney Generator

This study aims to provide a computational algorithm which contributes to the understanding and implementation of carbon nanochimneys. The structure resembles a tube ending with an inverted funnel, with a connection region that uses non-hexagonal rings as defects in order to match the boundaries of the two linked nanostructures. They are important for applications such as thermal transport, gas storage, or separation. The algorithm is written in Python 3.7 and provides a .pdb file with the coordinates of all the atoms included in the system. The parameters that can be specified are the carbon nanotube dimensions, for either armchair or zigzag conformations, five levels of disclination for the carbon nanocone along with the base diameter of the latter

Investigating Hydrogen Separation in a Novel Rotating Carbon Nanotube–Carbon Nanocone Setup Using Molecular Dynamics Simulations

Hydrogen fuel cells rely on the purity of the hydrogen gas for maintaining a high performance. This study investigates a novel nanostructure design for its effectiveness in separating H2 molecules from a mixture of gases containing H2, CH4, CO2, N2, CO and H2O molecules using Molecular Dynamics simulations. Based on an open-ended (28, 0) rotating carbon nanotube with one carbon nanocone at each of its two extremes, this device is predicted through Molecular Dynamics simulations to be able to separate hydrogen from a gas mixture contained within. The nanocones were placed with their tips inside the nanotube and the size of the open channel created between the two was controlled to find a configuration that allows hydrogen to pass while restricting the other gases. Although in need of optimization, we find it capable of high selectivity and highlight captivating gas behavior insights to help advance rational gas separation device development

Analysis of the resistance frame of the equipment for opening and compartmentalizing watering furrows, using 3D finite elements

The proposed article sets out the results of the finite element structural analysis for the open and compartmentalised watering furrow equipment (OCWFE). It uses a 3D structural model with 3D finite elements. The analysis set out in the article is made in order to determine the field of relative displacement and equivalent stress in the load-bearing structure of the OCWFE. The structural model is generated in CAD-CAM. For the structural analysis, it is necessary the CAE model, which is obtained from the CAD-CAM model, mainly by eliminating gaps and interferences, but also by the techniques of realizing the contact between the components of the assembly and a careful mashing of the structure. The structural model thus created is supported and loaded in accordance with the experimental results from the literature. The relative displacement field and the equivalent stress field within the resistance frame of the OCWFE is obtained following the linear static analysis. The field of relative displacements is used to assess the effects on the quality of the work performed. The equivalent stress field is used to estimate the safety factor of the structure, by reference to the flow stress of the material from which the structure is built

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages