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  • Cellular and single strut data
    Data Types:
    • Tabular Data
  • Data for a Fully 3D Modeling of Single-Phase Fluid Flow in Fractured-Vuggy Carbonate Formations Using the Transient Brinkman Equation Coupled with the Second-order Gradient Rock Mechanics Equation Including Porosity Evolution study
    Data Types:
    • Tabular Data
    • Dataset
  • proteomic data RP2
    Data Types:
    • Tabular Data
    • Dataset
  • Monthly Chinese cruise tourist volume and economic indexes (PMI, CCI and REERI) and weekly search query data from Baidu.
    Data Types:
    • Tabular Data
    • Dataset
  • This data provides the detailed test results of the benchmarking for the binary-classification performance metrics. The benchmark comprising three stages was applied on 13 metrics namely True Positive Rate, True Negative Rate, Positive Predictive Value, Negative Predictive Value, Accuracy, Informedness, Markedness, Balanced Accuracy, G, Normalized Mutual Information, F1, Cohen’s Kappa, and Mathew’s Correlation Coefficient (MCC). The new benchmarking method is described in Gürol Canbek, Tugba Taskaya Temizel, and Seref Sagiroglu, "BenchMetrics: A Systematic Benchmarking Method for Binary-Classification Performance Metrics", Information Processing & Management, 2020 (Submitted).
    Data Types:
    • Tabular Data
    • Dataset
  • IR-QUMA The IR-QUMA study (Iranian Survey on Quality in Messenger Apps) is defined to evaluate the quality of some messenger apps. A questionnaire was designed to evaluate some quality-related measures, metrics and features from users’ experience point of view. The questionnaire was published in popular channels of Iranian mobile social networks, in 10 different messengers (including Telegram, Whatsapp, Instagram, Eita, Soroush, Bale, Gap, IGap, Shaad and Rubika). More than 40 communities of users in these 10 messengers have contributed to this research questionnaire. Total of data is exceeds the level of 7k filled online forms. We hash the name of these messengers randomly by assigning ID-codes from M1 to M10.
    Data Types:
    • Tabular Data
    • Dataset
  • test
    Data Types:
    • Tabular Data
    • Dataset
  • WAXD and SAXS patterns of poly(ɛ-caprolactone) (PCL) monofilaments were recorded on a Bruker Nanostar U diffractometer (Bruker AXS, Karlsruhe, Germany) with a Cu-Kα radiation λ = 1.5419 Å and a VÅNTEC-2000 MikroGap area detector. Mechanical properties were measured with the tensile testing machine Statimat ME+ (Textechno, Germany). Thermal properties were characterized using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Rheological properties of the PCL polymer were characterized with the Rheometer Physica MCR 301 (Anton Paar), using a plate-plate geometry. The surface topography of fibers was analyzed using the scanning electron microscope (SEM) FE-SEM S-4800 (Hitachi High-Technologies Europe, Krefeld, Germany) with an acceleration voltage of 5.0 kV.
    Data Types:
    • Other
    • Image
    • Tabular Data
    • Dataset
    • Text
  • The present dataset is a collection of information about the biomechanical behavior and histological characterization of abdominal aortic aneurysms (AAA) harvested during the autopsy procedure. The primary hypothesis of the present research is: Do cadaveric AAA walls, when previously stressed by inflation, conserve significant resistance against tearing comparable to no previously stressed aortas described in the literature? Eight AAAs (6 fusiform and two saccular) were carefully dissected and had their branches ligated with cotton or prolene sutures. Each specimen was submitted to intraluminal pressurization, up to the rupture of their wall. This pressurization was made through the inflation of an air balloon inside the specimens up to their rupture. From the border of the rupture sites, and from the proximal (control sample 1) and distal (control sample 2) no dilated portions of each vessel, samples were harvested for uniaxial tensile tests, and histological analysis. The uniaxial tensile test utilized the INSTRON SPEC 2200 device and was coordinated by INSPEC software and SERIES IX software. The essential variables collected through this test are failure stress, failure tension, and failure strain. Each sample test generated a chart representing the relationship between stress and strain. The histological analysis included hematoxylin-eosin, Picrocirius, and Voerhoeff stains. Unfortunately, some samples were lost, especially during histological processing. A quantitative analysis (collagen and elastic fibers) was made using the software Pannoramic Viewer and Case Viewer.1 Notable findings: Even after being stretched/stressed up to their rupture, the specimens conserved uniaxial biomechanical properties comparable to AAA and normal aorta samples previously described in the literature by Monteiro e Nynomiya respectively.2,3 DATA DESCRIPTION: a) Biomechanical Data: As explained above, four samples were collected for each specimen, two from each side of the rupture border and two control samples, one from a proximal and a second from a distal region of the vessel. It is important to highlight here that some samples did not produce valid biomechanical tests, so they do not have their results included here. For each valid sample test, three documents are generated: 1. Stress X strain chart 2. Table (excel file containing all the values related to the stress X strain chart 3. A report from the Biomechanical test software containing details of the test All charts contain a notification in their left upper corner about the failure stress, strain and tension of each sample. b) Histological Data: The percentage of coverage of collagen fibers and elastin fibers is expressed in table I in decimal numbers (for example, 0.36 = 36%). Similarly to the sampling for biomechanical tests, four samples were harvested from each aorta, when it was feasible. Ps.: All Case C samples were lost during processing
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
    • Text
  • Processing speech in multi-speaker environments poses substantial challenges to the human perceptual and attention system. Moreover, different contexts may require employing different listening strategies. For instance, in some cases individuals pay attention Selectively to one speaker and attempt to ignore all other task-irrelevant sounds, whereas other contexts may require listeners to Distribute their attention among several speakers. Spatial and spectral acoustic cues both play an important role in assisting listeners to segregate concurrent speakers. However, how these cues interact with varying demands for allocating top-down attention is less clear. In the current study, we test and compare how spatial cues are utilized to benefit performance on these different types of attentional tasks. To this end, participants listened to a concoction of two or four speakers, presented either as emanating from different locations in space or with no spatial separation. In separate trials, participants were required to employ different listening strategies, and detect a target-word spoken either by one pre-defined speaker (Selective Attention) or spoken by any of the speakers (Distributed Attention). Results indicate that the presence of spatial cues improved performance, particularly in the two-speaker condition, which is in line with the important role of spatial cues in stream segregation. However, spatial cues provided similar benefits to performance under Selective and Distributed attention. This pattern suggests the despite the advantage of spatial cues for stream segregation, they were nonetheless insufficient for directing a more focused ‘attentional spotlight’ towards the location of a designated speaker in the Selective attention condition.
    Data Types:
    • Tabular Data
    • Dataset