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687 results for “systems analysis”
Figure 3 in Integrative analysis in toxicological assessment of the insecticide Malathion in Allium cepa L. system
Figure 3. Discriminant canonical function, showing the distribution of the centroids e of the groups of the different treatments; 1: treatment submitted to distilled water; 4: positive control with MMS; 2 and 3: groups exposed to Malathion, for 0.5 e 1.0 mg mL-1 concentrations, respectively.
Figure 2 in Integrative analysis in toxicological assessment of the insecticide Malathion in Allium cepa L. system
Figure 2. Mitotic index at the radicular meristematic region of Allium cepa cells, after exposure for 48 hours to distilled water (H Od – negative control), 0.5 mg mL-1, 1.0 mg mL-1 of Malathion 2 and methyl methanesulfonate (MMS – positive control). KW-H = results of Kruskal-Wallis test and p = value of the statistical probability. Letters on the error bars indicate the result of the statistical Mann-Whitney U test.
Supplementary online material for KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern. Eclipse modelling of the triple and spectroscopic analysis
<p>Additional figures and data supplementary to the published (or soon-to-be-published) paper KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern Eclipse modelling of the triple and spectroscopic analysis.</p> <p> </p>
Figure 1. (a) Classical set and (b) Fuzzy set 2.3.-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>A membership function is a curve that represents the degree of points which belong to the<br> specific fuzzy variable. Selecting the appropriate membership function plays an essential rule in<br> design of a fuzzy logic controller. The shape of membership function could be defined based on the<br> simplicity, convenience, speed and efficiency. Many different membership functions are introduced<br> in the literatures such as triangular, trapezoidal and Gaussian. The membership function which<br> represented in figure 1(b) is a trapezoidal type.</p>
Figure 2. Causal loop diagram for the problem situation-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>The problem situation could be represented in the following form of a causal loop diagram<br> as shown in Figure 2.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 3. Sentiment analysis on extracted themes
<p>Figure 3 is presenting the sentiment analysis results from the point of view of the themes extracted from the corpus. The same preoccupation for the cost of the course is revealed, but this time the fact that MOOCs are free is appreciated. Students perceive that an integration of MOOCs into blended courses leads to a rapid information of the topics, such a feature receiving a high positive score of +3.46. The detailed explanations in this blended approach received a positive impact from the students with a total score of +2.70, but also the gained knowledge is among the most highly rated corpus themes. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 2. Twitter sentiment analysis results
<p> In order to validate our results, the next step was to extract the sentiment analysis from Tweeter’s tweets (Figure 2) which are based on blending embedded systems-related courses. The obtained polarity is positive, so this results shows not only that students appreciated this in a positive manner, but also that the proposed technique for integrating MOOCs into embedded systems courses is a viable one. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 1. Semantria result
<p>In Figure 1 the Semantria output is presented, having a positive polarity, with a score of 0.218. What is interesting to note here are the keywords extracted from students’ feedback. They noticed the integration of MOOCs in the Embedded Systems course as positive due to the fact that the new information is perceived as easier and the gained knowledge seems to be valuable. Students are affected by too many concepts and also by the idea of paying for the course.</p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 3]
<p>3D micro surface data processed in ConfoMap v7.4.8633 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besançon, France).</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Data for "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain"
<p>Data for the "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain".</p> <p>All files except 'simulated_hypoxia.csv' contains both input and output data.</p>
Data, analysis scripts, simulations, and schematics files for "Position Sensitive Alpha Detector for an Associate Particle Imaging System"
<p>Measured data and analysis script, as well as, simulated data, input scripts, as well as, schematics for the readout board for our publication "Position Sensitive Alpha Detector for an Associate Particle Imaging System"</p>
Disk replacement log file examples from a very large RAID disk system for predictive maintenance analysis
<p>README.txt</p> <p>Maintenance example belonging to: </p> <p> The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance<br> Chapter 9, The Future of Maintenance (2019).<br> Lambert Schomaker, Michele Albano, Erkki Jantunen, Luis Lino Ferreira<br> River Publishers (DK)<br> ISBN: 9788793609853, e-ISBN: 9788793609846, https://doi.org/10.13052/rp-9788793609846</p> <p>The figure .pdf did not make it into the book. Here are the raw data, processed <br> logs and .gnu script to produce it.</p> <p>Data: event logs on disk failure in two racks of a huge RAID disk system (2009-2016).</p> <p>disks1.raw<br> disks2.raw</p> <p>Event logs to RC-filtered time series:<br> RC-filt-disks-log.c <br> do-RC-filter-to-make-spikes-more-visible (bash script)<br> --><br> disks1.log<br> disks2.log</p> <p>Constant (horizontal line) indicating the level where users experienced system-down time<br> Disrupted-operations-threshold</p> <p>disk-replacement-log.gnu<br> disk-replacement-log.pdf<br> </p>
Analysis of heritage stones and model wall paintings by pulsed laser excitation of Raman, laser-induced fluorescence and laser-induced breakdown spectroscopy signals with a hybrid system
<p>Analysis of heritage stone samples, alabaster, gypsum, limestone and marble, and model wall paintings was carried out with a laboratory, hybrid system based on the pulsed laser excitation of Raman, laser-induced fluorescence and laser-induced breakdown spectroscopy signals. The system is based on a nanosecond Q-switched Nd:YAG laser operating at its second (532 nm), third (355 nm) and fourth (266 nm) harmonics and a spectrograph coupled to a time-gated intensified charge coupled device for spectral analysis allowing detection with temporal resolution. For the stone samples, Raman spectra display the characteristic vibration modes of SO<sub>4</sub><sup>2-</sup> of calcium sulfate, in alabaster and gypsum, and of free CO<sub>3</sub><sup>2- </sup>of calcium carbonate, in limestone and marble. Simultaneously acquired laser-induced fluorescence spectra reveal characteristic bands that help to distinguish between heritage stone types. The elemental composition of stone samples is obtained by laser-induced breakdown spectroscopy upon excitation at 355 nm. Spectra of all stone samples reveal their elemental composition that includes Ca, Na, Mn and Sr and the presence of molecular species, such as CN, C<sub>2</sub> and CaO. Additional emission lines, ascribed to Mg, Si, Al and K, appear with different intensities according to the nature of the stone material. Model wall paintings, based on a red pigment, prepared as fresco or mixed with two different binders, were also studied. The complementary information provided by the three spectroscopic modes allows the identification of the pigment as red vermillion and of the different preparations based on the pigment alone or in mixtures with linseed oil and egg yolk binders.</p>
Fig. 4 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 4. Map showing the geographic distribution of three small mammal assemblage types predicted for the City of Calgary area by a multinomial logistic regression (MLR) model associating the environmental variables to assemblage types, developed from data collected in 2012 and 2013 (Liccioli et al., 2014). Note how large portion of BWM and NHP were classified as assemblage 1 as expected, but also large portion of FCPP, where it was not expected.
Fig. 1 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 1. Study sites for the characterization of the small mammal assemblages in urban Calgary, AB, Canada in 2012–2013, showing the location of five areas in Urban Calgary and detailed map of Bowmont, Southland Lowlands, and Weaselhead. Bowmont (BM), Fishcreek Provincial Park (FCPP), Nose Hill Park (NHP), Southland Lowlands (SL), and Weaselhead (WSH).
Fig. 2 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 2. Dendrograms derived from the Bray-Curtis similarity of small mammal assemblages in five parks and natural areas in urban Calgary, AB, Canada, 2012–2013. a) Dendrogram using abundance data and group-average clustering algorithm. The dashed line indicates the cluster cut-off line of 45% similarity. Symbols for each site indicate the prevalence of definitive hosts (EmDH) and presence (1) or absence (0) of infected small mammals (EmIH). b) Dendrogram using abundance data and complete-linkage clustering algorithm. Note how it is similar to the dendrogram using group-average algorithm. c) Dendrogram using proportion data and group-average clustering algorithm. Note how all BM sites are in single cluster and all NHP sites and most sites are in another cluster, similar to the dendrogram using abundance data.
Fig. 1 in Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host - parasite system from the Vaal River System, South Africa
Fig. 1. Map of the Vaal River showing the position of sampling sites (I: below Grootdraai Dam; II: Vaal Dam; III: below Vaal River Barrage; IV: Bloemhof Dam; V: below Vaalharts Weir; VI: Douglas Weir) along the Vaal River. The block (B) indicates the position of the Vaal River within South Africa and insert A indicates the position of South Africa shaded on the African continent.
Domain-Driven Design in Microservices-Based Systems Development: A Systematic Literature Review and Thematic Analysis [Dataset]
<p>This repository contains all artifacts related to the study: Domain-Driven Design in Microservices-Based Systems Development: A Systematic Literature Review and Thematic Analysis</p>
Dataset Artifact for paper "Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?"
<p>Artifacts for the paper titled <strong><em>Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?</em></strong>.</p> <p>This artifact repository contains 9 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>ID</strong></td> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>syn_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for Causal Discovery</td> </tr> <tr> <td>2</td> <td>syn_rcd.zip</td> <td>RCD10, and RCD50 datasets for Causal Discovery</td> </tr> <tr> <td>3</td> <td>syn_causil.zip</td> <td>CausIL10, and CausIL50 datasets for Causal Discovery</td> </tr> <tr> <td>4</td> <td>rca_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for RCA</td> </tr> <tr> <td>5</td> <td>rca_rcd.zip</td> <td>RCD10, and RCD50 datasets for RCA</td> </tr> <tr> <td>6</td> <td>online-boutique.zip</td> <td>Online Boutique dataset for RCA</td> </tr> <tr> <td>7</td> <td>sock-shop-1.zip</td> <td>Sock Shop 1 dataset for RCA</td> </tr> <tr> <td>8</td> <td>sock-shop-2.zip</td> <td>Sock Shop 2 dataset for RCA</td> </tr> <tr> <td>9</td> <td>train-ticket.zip</td> <td>Train Ticket dataset for RCA</td> </tr> </tbody> </table> <p>Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system). </p> <p><strong>Details about the generation of our datasets</strong></p> <p><em>1. Synthetic datasets</em></p> <p>We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:<br><br>1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. <span>From this DAG, time series data for each node is generated using a </span><span>vector auto-regression (VAR) model. A fault is injected into a node </span><span>by altering the noise term in the VAR model for two timestamps. <br></span><span><br>2. RCD data generator [25] uses the pyAgrum package [3] to generate </span><span>a random DAG based on a given number of nodes, subsequently </span><span>generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its </span><span>conditional probability distribution.<br><br>3. CausIL data generator [15] generates causal graphs and time series data that simulate </span><span>the behavior of microservice systems. It first constructs a DAG of </span><span>services and metrics based on domain knowledge, then generates </span><span>metric data for each node of the DAG using regressors trained on </span><span>real metrics data. Unlike the CIRCA and RCD data generators, the </span><span>CausIL data generator does not have the capability to inject faults.<br><br></span>To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods. </p> <p><em>2. Data collected from benchmark microservice systems </em></p> <p>We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.</p> <p></p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/RCAEval">https://github.com/phamquiluan/RCAEval</a>.</p> <p><strong>References</strong></p> <p>As in our paper.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.