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722 results for “use case”
FIGURE 11 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 11. Images of all samples taken from SMA 0014 "Jacques". A, femur (r), drilled on the anterior side. B, femur (r), drilled on the posterior side. C, tibia (r). For all samples, the bone tissue types are indicated to the left, and the number and patterns of the visible growth cycles are indicated to the right. Abbreviations; D: Bone tissue type D, E: Bone tissue type E, F: Bone tissue type F, MC: Medullary cavity, RA: Remodeled area.
FIGURE 14 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 14. Images of all hindlimb samples taken from SMA 0011 "Max". A, femur (l), drilled on the anterior side. B, femur (l), drilled on the posterior side. C, tibia (l). D, fibula (l). E, SMA M16/12-3 femur (l). For all samples, the bone tissue types are indicated to the left, and the number and patterns of the visible growth cycles are indicated to the right. Abbreviations; D: Bone tissue type D, E: Bone tissue type E, EFS: External fundamental system, F: Bone tissue type F, MC: Medullary cavity, RA: Remodeled area.
FIGURE 9 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 9. Images of all samples taken from the isolated bones SMA G47/87-1, G47/87-1 and G50/91-1. A, SMA G46/87-1 humerus (l). B, SMA G50/91-1 humerus (r). C, SMA G47/87-1 femur (l). For all samples, the bone tissue types are indicated to the left, and the number and patterns of the visible growth cycles are indicated to the right. Abbreviations; D: Bone tissue type D, E: Bone tissue type E, MC: Medullary cavity, RA: Remodeled area.
Fig. 6.- Anthrenus angustefasciatus final larval instar case. 6a in Behavioural and feeding observations of some Anthrenus Geoffroy, 1767 species (Coleoptera, Dermestidae) and identification using final larval instar cases
Fig. 6.- Anthrenus angustefasciatus final larval instar case. 6a.- Dorsal aspect (scale bar = 1 mm). 6b.- Lateral aspect (scale bar = 1 mm). 6c.- Head capsule (scale bar = 1 mm). 6d.- Arrow-headed hastisetae on terminal segments (scale bar = 100 µm).
Fig. 5.- Anthrenus amandae final larval instar case. 5a in Behavioural and feeding observations of some Anthrenus Geoffroy, 1767 species (Coleoptera, Dermestidae) and identification using final larval instar cases
Fig. 5.- Anthrenus amandae final larval instar case. 5a.– Dorsal aspect (scale bar = 1 mm). 5b.- Lateral aspect (scale bar = 1 mm). 5c.- Head capsule (scale bar = 1 mm). 5d.- Arrow-headed hastisetae on terminal segments (scale bar = 100 µm).
Fig. 7.- Anthrenus isabellinus final larval instar case. 7a in Behavioural and feeding observations of some Anthrenus Geoffroy, 1767 species (Coleoptera, Dermestidae) and identification using final larval instar cases
Fig. 7.- Anthrenus isabellinus final larval instar case. 7a.- Dorsal aspect (scale bar = 1 mm). 7b.- Lateral aspect (scale bar = 1 mm). 7c.- Head capsule (scale bar = 1 mm). 7d.- Arrow-headed hastisetae on terminal segments (scale bar = 100 µm).
Fig. 4.- Anthrenus amandae. 4a in Behavioural and feeding observations of some Anthrenus Geoffroy, 1767 species (Coleoptera, Dermestidae) and identification using final larval instar cases
Fig. 4.- Anthrenus amandae. 4a.– Quiescent in final larval instar case. 4b.– Rotation in final larval instar case to split sutures in head capsule to facilitate eclosion. Scale bars = 1 mm in both cases.
Fig. 1.- Dorsal aspect. 1a.- Anthrenus amandae. 1b.- Anthrenus angustefasciatus. 1c in Behavioural and feeding observations of some Anthrenus Geoffroy, 1767 species (Coleoptera, Dermestidae) and identification using final larval instar cases
Fig. 1.- Dorsal aspect. 1a.- Anthrenus amandae. 1b.- Anthrenus angustefasciatus. 1c.- Anthrenus isabellinus. Scale bar = 1 mm in all cases.
Datasets for sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer
<p><span>These datasets reflect two main scenarios (S1-S2) associated to the operation of a sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer. Details about are illustrated in Section 1.3 of the supporting document. These scenarios analyse the operation of the digital twin of the IEEE 9-bus system and the differential protection scheme under healthy conditions, cyber-attack on communication channels of IEC 61850 Sample Values (SVs) protocol, and a fault in HV side of a transformer in the power system. The scenarios are presented with selected time-series plots in Section 1.3, accompanied a detailed analysis of the processes included and an impact assessment. Thus, d</span><span>uring execution of each scenario, data such as electrical measurements were captured and are collected</span> in the form of the datasets presented here.</p> <p>Specifically, </p> <ul> <li>SUC3/S1 <strong>Differential protection operation during transformer fault</strong> corresponds to the dataset of first scenario (S1) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines the operation of differential protection scheme (implemented in Typhoon controller) for a HV/MV transformer. The protection scheme receives data sent through IEC 61850 SVs from the two sides of the transformer. Specifically, this dataset corresponds to the first scenario (S1) of SUC3, where a short-circuit occurred on the HV side of a HV/MV transformer of the system. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the sinusoidal measurements were recorder by OPAL-RT.</li> <li>SUC3/S2 <strong>MITM with FDI cyber-attack in the SVs of HV transformer side</strong> corresponds to the dataset of the second scanario (S2) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines a MITM with FDI cyber-attack is conducted on the measurements of the HV side of the transformer, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the differential protection scheme via IEC 61850 protocol. Section 1.3.1 of the supporting document provides more details about the scenario related to this<br>dataset. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the measurements from the sine waves were recorder by OPAL-RT.</li> </ul>
Entice Optimisation Data for FlexiOPS Use Case
<p>The optimisation data for the project Entice shows the metrics gathered by FlexiOps in their use case. These measurements were taken in the Flexiant Cloud Orchestrator platform and shows how the Entice software optimises virtual machine images and reduces them considerably in size.</p>
Cybercrime search index – Use Case n.3
<p>Test data created for SUNFISH project UK Use Case testing and evaluation </p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 9. Proportion of indicators used in the case studies
<p>Figure 9 presents the indicators used for evaluating the effectiveness of mobile and ubiquitous learning practices. Learning achievements (64%) and perceived usefulness (56%) were the two most frequently used, followed by motivation (26%), ease of use (26%) and satisfaction (24%).<br> cognitive load (12%), system usage (8%), self-efficacy (6%) and social engagement (2%). Those indicators were usually adopted in the studies using qualitative methods for data collection. The results suggest a possible relationship between the data collection methods and the indicators. The choices of indicators represent, in principle, how the effectiveness of mobile and ubiquitous learning practices can be most appropriately evaluated and presented using particular study methods.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 8. Functions of the mobile devices used in the practices (Note: Each case could involve the use of more than one function.)
<p>Figure 8 captures the functions of mobile devices used in the practices. The results show that tailor-made applications for specific practices were most common (74%), followed by the use of a speaker (32%) and a camera (26%), where learners had to listen to audio materials using speakers or access online information by scanning QR-codes through cameras. In the various practices, other functions were also used, such as messaging (16%) for interacting with diverse parties and GPS (14%) for outdoor learning activities. For the practices using older models of mobile devices without cameras, tools such as an RFID reader (8%) were used for accessing information via communication tags.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
Data supporting the publication "Multi-use of the sea: a wide array of opportunities from site-specific cases across Europe"
<p>Data supporting the publication "Multi-use of the sea: a wide array of opportunities from site-specific cases across Europe"</p>
Vision dataset for ColRobot WP6 use-case
<p>A specific 2D sensing system, comprising of a 2D camera, a backlit feeder and its associated processing has been developed for the ColRobot kitting use-case. This sensing system is integrated with the gripper and the robot.</p> <p>The objective of the sensing system is to detect and locate the kitting parts (notably screws, washers and nuts) in order to grasp them.</p> <p>The sensing system must be able:</p> <ul> <li>To be rapidly reconfigured for parts of new dimensions (in case of evolution of the kitting specifications), without reprogramming.</li> <li>To be able to isolate parts among similar (but not identical) parts.</li> <li>To be able to avoid detecting parts that are overlapping.</li> </ul> <p>To be able to avoid detecting parts that are too close to each other (clearance should be above 6mm).</p> <p>Constraints are imposed on the processing algorithm to avoid grasping issues:</p> <ul> <li>Do not detect overlapping parts.</li> <li>Do not detect parts too close to another part (identical or not), i.e. keep 6mm clearance around any detected part.</li> </ul> <p>All input pictures are taken with the same sensor, at the same distance to the backlit feeder.</p> <p>Processing parameters are the same for all images.</p> <p>The list of parts to be detected with their parameters is given below in the Data input section.</p> <p>For all input images, all parts given in the input parameters are searched for and an output image is created if found highlighting a point of interest of the part, and an associated oriented frame.</p> <p>On some input images, additional (non-referenced) parts are present in order to test the discrimination properties of the algorithm (false positives).</p> <p>The dataset takes the following format:</p> <ul> <li>Input data: <ul> <li>List of input images.</li> <li>Table of parameters to be given to the processing algorithm for each input image. <ul> <li>For each part, a part type, and an ordered list of dimensions (semantics depending on the part type).</li> </ul> </li> </ul> </li> <li>Output data: <ul> <li>A structured folder of output images <ul> <li>For each input images, a clone of the input image is created for each part described in the input parameters, only if found, and highlighting: <ul> <li>A point of interest (static in the part frame for a given part type).</li> <li>An associated frame describing the part orientation.</li> </ul> </li> </ul> </li> </ul> </li> </ul>
JCDL 2019, ORKG, use case dataset
<p>DILS 2019 use-case dataset, collected via subject matter experts to represent DILS 2019 papers as a machine readable graph model</p>
APRSuite: A Suite of Components and Use Cases Based on Categorical Decomposition of Automatic Program Repair Techniques and Tools
<p><strong>During the last decade, we are witnessing the advent of a proliferation of techniques and associated tools for automatic program repair (APR). The current techniques and tools provide rich sources of knowledge that should be taken into consideration for future research. An overview of the current APR techniques and tools can serve the research community as a knowledge accumulator. However, APR techniques and tools differ in many aspects making knowledge accumulation challenging. To overcome this challenge, in this paper, we propose to leverage common components that constitute the APR techniques and tools. To achieve this objective, we surveyed current APR techniques and tools to identify the APR Suite of common constituent components, namely as APRSuite. Repair source and defect class are examples of identified components. We grouped these components into several categories such as patch evaluation and target defects. We have also identified some of the possible use cases per component as well as different lessons learned in studies for each component and for each use case. In addition, we developed a principled way for application of the components. The <em>APRSuite</em> and the <em>principled way</em> to apply it comprise a <em>framework</em> for knowledge accumulation, evaluation, and comparison of APR techniques and tools. The novelty of our work lies in its original viewpoint to the process of literature review in the APR research field. To demonstrate the applicability of the framework, we mapped out several concrete APR techniques, as a first instantiation of the framework. We observed that the framework brings discipline into the evaluation and/or comparison of APR techniques and tools. The framework offers these benefits objectively and systematically. We concluded that knowledge accumulation and characterization through literature reviews can be therefore facilitated through the identified suite of components while at the same time the existing component suite can be modified, augmented, or improved.</strong></p>
Fig. 9. Core ARD 3 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 9. Core ARD 3 selected percentage testate amoebae diagram, data are presented as percentages of the total testates in each level. The diagram has been subdivided into zones to better aid interpretation. Note that in the older literature (including all the more accessible identification guides) Archerella flavum is refered to as Amphitrema flavum.
ScienceDex guides
Understand access before you commit
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.