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39 results for “Data Flow Analysis”
Scaler: Efficient and Effective Cross Flow Analysis (Pre-recorded data for perf)
<p>This is a dataset complement to https://doi.org/10.5281/zenodo.13336658.</p> <p>All the experiments run on the host machine with the same evaluation script and benchmark applications.</p> <p>We provided this because perf report has a bug and may fail to resolve the symbol inside the docker container.</p> <p> </p>
Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019)
<p>Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019) by </p> <p><span>Adam R. Mason <sup>1,a</sup>, Tom Bide <sup>2,b</sup>, Junyang Wang <sup>3,c</sup>, John Morley <sup>4,d</sup>, Mohit Arora <sup>5,e</sup>, Alperen Yayla<sup>1,f</sup>, Julia A. Stegemann <sup>5,g</sup>, Rupert J. Myers <sup>1,h,*</sup></span></p> <p><span> </span></p> <p><sup><span>1</span></sup><span> Department of Civil and Environmental Engineering, Imperial College London, UK</span></p> <p><sup><span>2</span></sup><span> British Geological Survey, UK</span></p> <p><sup><span>3 </span></sup><span>Department of Mathematics, Imperial College London, UK</span></p> <p><sup><span>4</span></sup><sub><span> </span></sub><span>Department of Earth Science and Engineering, Imperial College London, UK</span></p> <p><sup><span>5</span></sup><span> School of Engineering, King’s College London, UK</span></p> <p><sup><span>6</span></sup><span> Department of Civil, Environmental and Geomatic Engineering, University College London, UK</span></p> <p><span> </span></p> <p><span>Author e-mails: <sup>a </sup></span><a href="mailto:a.mason19@imperial.ac.uk"><span>a.mason19@imperial.ac.uk</span></a><span>,<sup> b </sup></span><a href="mailto:tode@bgs.ac.uk"><span>tode@bgs.ac.uk</span></a><span>,<sup> c </sup></span><a href="mailto:junyang.wang21@imperial.ac.uk"><span>junyang.wang21@imperial.ac.uk</span></a><span>,<sup> d </sup></span><a href="mailto:john.morley18@imperial.ac.uk"><span>john.morley18@imperial.ac.uk</span></a><span>,<sup> e </sup></span><a href="mailto:mohit.arora@kcl.ac.uk"><span>mohit.arora@kcl.ac.uk</span></a><span>,<sup> f </sup></span><a href="mailto:a.yayla22@imperial.ac.uk"><span>a.yayla22@imperial.ac.uk</span></a><span>,<sup> g </sup></span><a href="mailto:j.stegemann@ucl.ac.uk"><span>j.stegemann@ucl.ac.uk</span></a><span>; *corresponding author:<sup> h</sup> </span><a href="mailto:r.myers@imperial.ac.uk"><span>r.myers@imperial.ac.uk</span></a></p> <p> </p>
Supplementary Material for "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis"
<p>Supplementary material for the paper "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://dataflowanalysis.org</p>
Enhancing Distributed Summary Synthesis with Data-Flow Analysis
<p>With increasing software complexity, scalable and precise verification is essential, especially in safety-critical areas. Distributed Summary Synthesis (DSS) supports scalability by enabling parallel processing of program segments (blocks). However, it faces limitations in achieving early-stage abstraction due to the inherent laziness of Predicate Analysis, which only refines abstractions when errors are detected. This thesis addresses this by integrating Data-Flow Analysis (DFA) into DSS, enhancing the initial information shared among program blocks to potentially accelerate and improve verification. Implemented in CPAchecker, DFA runs in parallel with Predicate Analysis, providing coarse summaries that strengthen the preconditions for successor blocks. Experimental evaluation using SV-COMP 2024 benchmarks, however, indicated that while DFA integration occasionally improved verification coverage, it also introduced additional resource demands. This increase in CPU time, wall time and memory usage, due to message handling and serialization and deserialization overhead, limited the number of programs that could be verified compared to the DSS implementation with only predicate analysis. This trade-off suggests that additional optimizations are needed to reduce performance costs and better harness the potential of DFA for scalable and effective verification.<br><br></p>
Dataset: Tool-Supported Architecture-Based Data Flow Analysis for Confidentiality
<p>Dataset for Paper "Tool-Supported Software Architecture-Based Data Flow Analysis for Confidentiality".</p>
MarTREC Data Set for Report: Developing and Applying an Analysis Methodology to Identify Flow Generation Influences between Vessel and Truck Shipments
<p>Truck activity is logically connected to vessel activity at a port. In turn, vessel activity is also influenced by truck shipments. Although one might expect a direct and straightforward relation between these two types of shipments, that is rarely the case. For instance, many maritime containers carry consolidated cargos that have multiple and different final destinations. Also, different truck capacities, customs clearance and regulations play a critical role in determining the actual relation between these two types of shipments. This project aims at shedding light on the nuances of maritime and roadway flow relations by quantitatively analyzing the linkages between these two types of shipments.</p> <p>The study performed a statistical analysis to determine the probability distributions of vessel and truck activity, and then explore the correlation of each activity with the other. The analysis yielded coefficients that function as explanatory values for specific truck flows.</p> <p>The ultimate purpose of this study is to provide a clearer and quantitative understanding of the relationship between maritime and truck shipments, and by doing so, to provide tools to develop a system for managing trucks that maximizes efficiency for industry, while minimizing industry’s negative impacts on a region.</p> <p>For this purpose, the study selected the Port Freeport as a case study.</p>
Data from: Postpartum family planning integration with maternal, newborn, and child health services: a cross-sectional analysis of client flow patterns in India and Kenya
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Data from: Pollinator-mediated gene flow connects green roof populations across the urban matrix: a paternity analysis of the self-compatible forb Penstemon hirsutus
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Data from: A multi-scale analysis of gene flow for the New England cottontail, an imperiled habitat specialist in a fragmented landscape
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Data from: Thank you for not flowering: conservation genetics and gene flow analysis of native and non-native populations of Fraxinus (Oleaceae) in Ireland
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Data from: Flow cytometry combined with viSNE for analysis of microbial biofilms and detection of microplastics
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Data from: Spatiotemporal analysis of gene flow in Chesapeake Bay Diamondback Terrapins (Malaclemys terrapin)
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Data from: Flow cytometric methods for indirect analysis and quantification of gametogenesis in Chlamydomonas reinhardtii (Chlorophyceae)
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Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery
GEO Series GSE162177. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Three-dimensional hydraulic tomography analysis of long-term municipal wellfield operations: Validation with synthetic flow and solute transport data
<p>Attached are the datasets utilized to accomplish the study of "<em>Three-dimensional hydraulic tomography analysis of long-term municipal wellfiled operations: Validation with synthetic flow and solute transport data". </em>Basically, it includes the information of the discrete model and assigned, wells as well as the generated synthetic hydraulic head and solute concentration data.</p>
Contextualisation of Data Flow Diagrams for security analysis: Pilot study model
<p>CAIRIS model package to accompany 'Contextualisation of Data Flow Diagrams for security analysis' GraMSec 2020 paper. </p> <p>The model package can be loaded in CAIRIS via the System >> Import Model menu.</p>
Flow cytometry data of intestinal immune cells related to Seumetry toolkit for analysis of high-dimensional flow and mass cytometry data
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Data from: Microstructure and cerebral blood flow within white matter of the human brain: a TBSS analysis
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Data for the manuscript "Classification of Stream, Hyperconcentrated, and Debris Flow Using Dimensional Analysis and Machine Learning"
<p>The excel file contains hydrological and sediment data. Also included are dimensional analysis data in our dataset.<br> The rar file contains the codes and data for SVM classification work.</p>
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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
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