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25 results for “model-driven”
Supplementary data for Model-driven engineering of Cutaneotrichosporon oleaginosus ATCC 20509 for improved microbial oil production
<p>Supplementary data corresponding to manuscript named Model-driven engineering of <em>Cutaneotrichosporon oleaginosus</em> ATCC 20509 for improved microbial oil production. </p> <p>The Supplementary material document contains supplementary figures and tables. The content of the figures and tables are indicated below. </p> <ul> <li>Figure S1. Plasmid map of pUC57NAT containing pGpd, nourseothricin acyltransferase gene and tGpd.</li> <li>Figure S2. Plasmid maps of overexpression targets containing TEF1α promoter, ATP-citrate lyase gene, TEF1α terminator, TPI1 promoter, Acetyl-CoA carboxylase gene, TPI1, YAT1 promoter, threonine synthase gene, YAT1 terminator and ENO1 promoter, hydroxymethylglutaryl-CoA synthase gene, ENO1 terminator.</li> <li>Table S2. Nucleotide sequences of promoters, genes, and terminators from <em>C. oleaginosus.</em></li> <li>Figure S3. Calibration curve of glycerol for calculating the glycerol concentration of medium.</li> <li>Figure S4. Volcano plots displaying differentially expressed genes and fold change (log2) in expression levels in WT, Δ9 and Δ12 strains at low lipid accumulation vs high lipid accumulation conditions.</li> <li>Figure S5. Flux distribution graphs of selected reactions for overexpression in C. oleaginosus.</li> <li>Figure S6. Colony PCR products were run on 1 % agarose gel. The colony PCR was performed for WT, ACL, ACC and TS transformants.</li> <li>Table S4. qPCR outputs, CT: The threshold cycle.</li> <li>Table S5. Fatty acid profile of C. oleaginosus grown at minimal medium with or without supplement (biotin, thiamine, threonine, serine, and aspartate) at 96h.</li> <li>Table S6. Lipid content, dry cell weight, and lipid weight of WT, ACL, ACC, TS, and HMGS <em>C. oleaginosus</em> at various C/N ratio minimal medium.</li> <li>Table S7. Fatty acid profile of WT, ACL, ACC, HMGS, and TS grown at C/N30, 120, 175, 200, and 300 minimal medium at 96h.</li> <li>Figure S7. Quadratic regression analysis on lipid accumulation, biomass and lipid content of wild-type, ACL, ACC, and TS C. oleaginosus at C/N 30, 120, 175, 200, 300.</li> <li>Table S8. Regression equations, statistics of regression equations for lipid content, biomass, and lipid content of wild-type, ACL, ACC, and TS.</li> <li>Table S9. Calculated optimum C/N ratios and responses (lipid content, biomass, and total lipid) by using built regression models for wild-type, ACL, ACC, and TS.</li> </ul> <p>Authors: </p> <p>Zeynep Efsun Duman-Özdamar<sup>a,b,c</sup>, Mattijs K. Julsing<sup>c</sup>, Janine A.C. Verbokkem<sup>c</sup>, Emil Wolbert<sup>c</sup>, Vitor A.P. Martins dos Santos<sup>a,b,d</sup>, Jeroen Hugenholtz<sup>e,f</sup>, Maria Suarez-Diez<sup>b*</sup></p> <p><sup>a</sup>Bioprocess Engineering, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands</p> <p><sup>b</sup>Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE, Wageningen, the Netherlands</p> <p><sup>c</sup>Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands</p> <p><sup>d</sup>LifeGlimmer GmbH, Berlin, 12163, Germany</p> <p><sup>e</sup>Faculty of Science Swammerdam Institute for Life Sciences, University of Amsterdam, 1090 GE Amsterdam, The Netherlands</p> <p><sup>f</sup>NoPalm Ingredients BV, 6709 PA Wageningen, The Netherlands</p>
Model-driven mitigation measures for reopening schools during the COVID-19 pandemic.
<p>Complete simulation-generated datasets analyzed in McGee et al. (2021) Model-driven mitigation measures for reopening schools during the COVID-19 pandemic. PNAS. In press at time of upload. (medRxiv 2021.01.22.21250282).</p> <p>Data is uploaded in tab-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.</p>
Model-driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1440 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_null_D_0.95_E_F.mat</p> <p>Where the fields A, B, C, D, E, and F are described as follows.</p> <p>A: number of users.<br /> Considered values are: 2500, 5000, 10000, 20000, 40000.</p> <p>B: variation in the profile of the requests.<br /> Considered values are: ref, var1, var2, var3, var4, var5.<br /> ref -> reference experiment, no users are halved or doubled<br /> var1 -> users in class 1 halved, other users doubled <br /> var2 -> users in class 2 halved, other users doubled <br /> var3 -> users in class 3 halved, other users doubled <br /> var4 -> users in class 4 halved, other users doubled <br /> var5 -> users in class 5 halved, other users doubled </p> <p>C: variation of the replaceability set of the application server.<br /> Considered values are: ref, k1, k2, k3, k4, k5.<br /> ref -> reference experiment, no rates are halved or doubled<br /> k1 -> only possible substitution has rate k1 halved and other rates doubled<br /> k2 -> only possible substitution has rate k2 halved and other rates doubled <br /> k3 -> only possible substitution has rate k3 halved and other rates doubled <br /> k4 -> only possible substitution has rate k4 halved and other rates doubled <br /> k5 -> only possible substitution has rate k5 halved and other rates doubled </p> <p>D: variation of the design constraints.<br /> Considered values are: none, 4, 34, 234.<br /> none -> no components can be replicated<br /> 4 -> only the application server can be replicated<br /> 34 -> only the application server and the database server can be replicated<br /> 234 -> all the components can be replicated</p> <p>E: Optimization algorithm.<br /> Considered values are: norefactoring, replacement, reassignment, full<br /> norefactoring -> experiment with no refactorings<br /> replacement -> experiment with only replacement refactoring<br /> reassignment -> experiment with only reassignment refactoring<br /> full -> experiment with replacement and reassignment refactorings</p> <p>F: Experiment seed.<br /> Considered values are from 1 to 20</p> <p> </p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
Replication Package: Model-Driven Engineering for the Interoperability of Simulation Modeling Languages: a Case Study in the Space Industry
<p>Replication package "Architectural Support for Software Performance in Continuous Software Engineering: a Systematic Mapping Study".</p>
Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)
<p>Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"</p>
Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms - SLR Dataset
<p>Dataset of the publications collected at various stages of the Structured Literature Review titled "<a href="https://link.springer.com/article/10.1007/s10270-023-01109-1">Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms</a>" published in Software and Systems Modeling:</p> <p>The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package
<div> <p><strong>Title:</strong> A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the replication package artefact for the paper entitled "A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures".</p> <p><strong>Paper Abstract:</strong> In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.</p> </div>
Artifact for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building"
<p>This is a reproducibility package for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building".</p> <p>Domestic heating systems can provide significant energy flexibility when integrated with heat pumps and hot water buffer tanks, especially with fluctuating day-ahead energy prices. However, optimizing these systems in large buildings with shared resources poses crucial challenges. While most existing studies target single-room or single-family house systems, this study explores the complexities within a three-story building housing six apartments. The building’s heating system consists of a hot water buffer tank, mixing loop, radiant floor heating system, and a Ground Source Heat Pump (GSHP) controlled by a weather-compensated control strategy (WCS). Our approach aims to tackle challenges like integrating real sensor data, scalability, varying weather effects, and diverse resident heat use preferences. We employ the CTSMR software to identify thermal behaviour and use reinforcement learning to design an intelligent/model-driven UPPAAL STRATEGO<br>controller. Our results reveal a 43% reduction in energy costs while maintaining comfort levels compared to a WCS. The temporal validity of the estimated thermal models is also analyzed.</p>
Replication package for "Blended Modeling in Commercial and Open-source Model-Driven Software Engineering Tools: A Systematic Study"
<p>Replication package for the paper <em>Blended Modeling in Commercial and Open-source Model-Driven Software Engineering Tools: A Systematic Study</em>.</p> <p>Protocol</p> <ul> <li><code>/01-protocol/protocol.pdf</code></li> </ul> <p>Data & analysis scripts</p> <p>This replication package is structured as follows:</p> <ul> <li><code>/02-search</code> - Detailed data on the <code>/academic</code> and <code>/grey literature</code> search.</li> <li><code>/03-tools</code> - Identified tools and inclusion/exclusion decisions.</li> <li><code>/04-classification_schema</code> - Classification framework and the corresponding data extraction form.</li> <li><code>/05-data</code> - Clean data in a processable form.</li> <li><code>/06-analysis</code> - Analysis scripts and results.</li> </ul>
Practitioners' Experiences with Model-Driven Engineering: A Meta-Review
<p>These files contain the data used for the selection of publications for the meta-review, and the summarized findings from the selected papers.</p>
A flexible operation-based infrastructure for collaborative model-driven engineering (Evaluation Data)
<p>Repository containing the UML models used in the evaluation of the paper entitled: "A flexible operation-based infrastructure for collaborative model-driven engineering" submitted to ECMFA 2023.</p>
ATOM: Model-Driven Autoscaling for Microservices
<p>This dataset release supports the results presented in the paper<br> "ATOM: Model-Driven Autoscaling for Microservices", by A. U. Gias, G. Casale and M. Woodside, accepted in IEEE International Conference on Distributed Computing Systems (ICDCS), 2019. </p> <p>When referring to the dataset please cite the paper above.</p> <p> </p>
A Model-driven and Template-based Approach for Requirements Specification : Case studies
<p>This dataset includes the requirements specified using our templates as part of our case studies :</p> <ol> <li>ARINC-653 case study : Time management requirements.</li> <li>ARINC-653 case study : Intra-partition communication requirements.</li> <li>AUTOSAR case study : Crypto service manager requirements.</li> <li>Traditional software case study : Requirements from Kaggle.</li> </ol>
Digital Twins for Model-driven Non-invasive Electrical Brain Stimulation
ClinicalTrials.gov study NCT06826261. IPD Sharing: YES. Countries: 1. Publications: 22.
Model-Driven Individualized Transcranial Direct Current Stimulation for the Treatment of Insomnia Disorders
ClinicalTrials.gov study NCT06671457. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Replication Package for A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems
<p>This package contains supplemental material for the paper "A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems".</p> <p>In particular, we provide:</p> <ul> <li>The survey protocol</li> <li>The used search strings</li> <li>The search results for each library</li> <li>The data extraction template</li> <li>The data extraction sheet for each selected approach</li> <li>The list of all publications and their exclusion stage</li> </ul>
Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation
GEO Series GSE39785. Mus musculus. 16 samples. Type: Expression profiling by array.
Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation
GEO Series GSE60398. Cryptococcus neoformans. 320 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.