3D Printing Complex Structures Using Modeling and Simulation

2015 ◽  
Author(s):  
Hammad Mazhar
Keyword(s):  
3D Printing ◽  
Open Source ◽  
Data Structures ◽  
Large Scale ◽  
Selective Layer ◽  
Parallel Data ◽  
Wide Range ◽  
Data Structures And Algorithms ◽  
Multi Body ◽  
Dynamics Problems

This paper describes an open source parallel simulation framework capable of simulating large-scale granular and multi-body dynamics problems. This framework, called Chrono::Parallel, builds upon the modeling capabilities of Chrono::Engine, another open source simulation package, and leverages parallel data structures to enable scalable simulation of large problems. Chrono::Parallel is somewhat unique in that it was designed from the ground up to leverage parallel data structures and algorithms so that it scales across a wide range of computer architectures and yet has a rich modeling capability for simulating many different types of problems. The modeling capabilities of Chrono::Parallel will be demonstrated in the context of additive manufacturing and 3D printing by modeling the Selective Layer Sintering layering process and simulating large complex interlocking structures which require compression and folding to fit into a 3D printer’s build volume.

scholarly journals Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

2021 ◽  
pp. 47-59
Author(s):  
Sacha J. van Albada ◽  
Jari Pronold ◽  
Alexander van Meegen ◽  
Markus Diesmann
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Network Models ◽  
Macaque Monkey ◽  
Source Model ◽  
Model Specification ◽  
Data Sets ◽  
Neural Network Models ◽  
Wide Range ◽  
Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

COMPUTATIONAL FEATURES OF AGENT-BASED MODELS

2005 ◽  
Vol 02 (01) ◽  
pp. 33-48 ◽  
Author(s):  
MASSIMO BERNASCHI ◽  
FILIPPO CASTIGLIONE
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Agent Based Modeling ◽  
System Response ◽  
Agent Based Models ◽  
Design Data ◽  
Agent Based ◽  
Data Structures And Algorithms ◽  
Immune System Response ◽  
Large Scale Simulations

Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

scholarly journals Application of Multi-Material 3D Printing for Improved Functionality and Modularity of Open Source Low-Cost Prosthetics: A Case Study

2017 ◽  
Author(s):  
Sachin Bijadi ◽  
Erik de Bruijn ◽  
Erik Y. Tempelman ◽  
Jos Oberdorf
Keyword(s):  
3D Printing ◽  
Open Source ◽  
Low Cost ◽  
Design Approach ◽  
Prosthetic Hand ◽  
User Centered Design ◽  
Manufacturing Method ◽  
Wide Range ◽  
Modular Platform ◽  
Traditional Manufacturing

Low-cost 3D desktop printing, although still in its infancy, is rapidly maturing, with a wide range of applications. With its ease of production and affordability, it has led to development of a global maker culture, with the design and manufacture of artefacts by individuals as a collaborative & creative hobbyist practice. This has enabled mass customization of goods with the potential to disrupt conventional manufacturing, giving more people access to traditionally expensive products like prosthetics and medical devices [1], as is the case with e-NABLE, a global community providing open source prosthetics for people with upper limb deficiencies. However one of the major barriers to proliferation of 3D printing as a major manufacturing method is the limitation of compatible materials for use with the technology [2]. This places constraints on the design approach, as well as the complexity & functionality of artefacts that can be produced with 3D printing as compared to traditional manufacturing methods. As a result, devices like the e-NABLE Raptor Reloaded prosthetic hand, which is designed specifically to be produced via a single extruder FDM desktop 3D printer, have limited functionality as compared to conventional prosthetics, leading to low active use and prosthesis abandonment [3]. However, with the advent of multi-material desktop 3D printing, and increasing availability of a broader range of compatible materials (of varying characteristics) [2], there is scope for improving capabilities of low-cost prosthetics through the creation of more sophisticated multi-material functional integrated devices. This work documents the exploration of potential applications of multi-material 3D printing to improve production, capabilities and usability of low-cost open source prosthetics. Various material combinations were initially studied and functional enhancements for current 3D printed prosthetics were prototyped using key material combinations identified. Further, a user-centered design approach was utilized to develop a novel multi-material anthropomorphic prosthetic hand ‘ex_machina’ based on a modular platform architecture, to demonstrate the scope for reduced build complexity and improved dexterity & functional customization enabled by dual extrusion FDM desktop 3D printing. A full prototype was built & tested with a lead user, and results analyzed to determine scope for optimization.

scholarly journals Gsmodutils: a python based framework for test-driven genome scale metabolic model development

2019 ◽  
Vol 35 (18) ◽  
pp. 3397-3403 ◽  
Author(s):  
James Gilbert ◽  
Nicole Pearcy ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Model Development ◽  
Metabolic Model ◽  
Supplementary Information ◽  
Validation Data ◽  
Engineering Research ◽  
Modelling Framework ◽  
Wide Range ◽  
Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.Availability and implementationThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals RADAR-base: An Open Source mHealth Platform for Collecting, Monitoring and Analyzing Data Using Sensors, Wearables, and Mobile Devices (Preprint)

2018 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Maximilian Kerz ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
User Interfaces ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
Multiple Sources ◽  
High Resolution Data ◽  
Wide Range

BACKGROUND With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable and extensible platform is of high interest to the open source mHealth community. The EU IMI RADAR-CNS program is an exemplar project with the requirements to support collection of high resolution data at scale; as such, the RADAR-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. OBJECTIVE Wide-bandwidth networks, smartphone penetrance and wearable sensors offer new possibilities for collecting (near) real-time high resolution datasets from large numbers of participants. We aimed to build a platform that would cater for large scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security and privacy. METHODS RADAR-base is developed as a modular application, the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides two main mobile apps for data collection, a Passive App and an Active App. Other 3rd Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. RESULTS General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy and Depression cohorts. CONCLUSIONS RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals Gsmodutils: A python based framework for test-driven genome scale metabolic model development

2018 ◽  
Author(s):  
James P Gilbert ◽  
Nicole Pearcy ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Model Development ◽  
Metabolic Model ◽  
Validation Data ◽  
Engineering Research ◽  
Modelling Framework ◽  
Wide Range ◽  
Scale Models ◽  
Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed thegsmodutilsmodelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.AvailabilityThe software framework described within this paper is open source and freely available fromhttp://github.com/SBRCNottingham/gsmodutils

scholarly journals SPECTRE: a Suite of PhylogEnetiC Tools for Reticulate Evolution

2017 ◽  
Author(s):  
Sarah Bastkowski ◽  
Daniel Mapleson ◽  
Andreas Spillner ◽  
Taoyang Wu ◽  
Monika Balvočiūtė ◽  
...  
Keyword(s):  
Open Source ◽  
Data Structures ◽  
Phylogenetic Trees ◽  
High Performance ◽  
Reticulate Evolution ◽  
Supplementary Information ◽  
Link Type ◽  
Data Structures And Algorithms ◽  
User Friendly ◽  
Split Networks

ABSTRACTSummarySplit-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing (HPC) environments.AvailabilityFull source code is available under the GPLv3 license at: https://github.com/maplesond/SPECTRESPECTRE’s core library is available from Maven Central at: https://mvnrepository.com/artifactuk.ac.uea.cmp.spectre/coreDocumentation is available at: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/[email protected] Information (SI)Supplementary information is available at Bioinformatics online.

Electron microscopy study of reactively sputter-deposited Ti/N films on silicon

1992 ◽  
Vol 50 (2) ◽  
pp. 1362-1363
Author(s):  
V. C. Kannan ◽  
A. K. Singh ◽  
R. B. Irwin ◽  
S. Chittipeddi ◽  
F. D. Nkansah ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Large Scale ◽  
Hexagonal Phase ◽  
Microscopy Study ◽  
Electron Microscopy Study ◽  
Process Conditions ◽  
Tin Films ◽  
Wide Range ◽  
Fcc Phase ◽  
Circuits Vlsi

Titanium nitride (TiN) films have historically been used as diffusion barrier between silicon and aluminum, as an adhesion layer for tungsten deposition and as an interconnect material etc. Recently, the role of TiN films as contact barriers in very large scale silicon integrated circuits (VLSI) has been extensively studied. TiN films have resistivities on the order of 20μ Ω-cm which is much lower than that of titanium (nearly 66μ Ω-cm). Deposited TiN films show resistivities which vary from 20 to 100μ Ω-cm depending upon the type of deposition and process conditions. TiNx is known to have a NaCl type crystal structure for a wide range of compositions. Change in color from metallic luster to gold reflects the stabilization of the TiNx (FCC) phase over the close packed Ti(N) hexagonal phase. It was found that TiN (1:1) ideal composition with the FCC (NaCl-type) structure gives the best electrical property.

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