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261 results for “Performance Analysis”
Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
<p>Dataset of the results published in the <a href="https://zenodo.org/record/4742379#.YMzExOgzYmJ">J. Mater. Chem. A, 2021, 9, 10466</a>. The files represent: i) the measured compositional and optoelectronic data of each solar cell, as well as the data generated from the Raman spectra analysis; ii) Raman spectra of the representative cells; iii) Machine Learning discriminants.</p> <p>The elemental composition of the different cells of the combinatorial sample was determined by X-ray fluorescence (XRF) using a Fischerscope XDV system with a 1 mm spot diameter, a 50 kV acceleration voltage, a Ni10 lter and a 45 s acquisition time. Raman analysis with blue (442 nm) and green (532 nm) excitation wavelengths were performed on the bare absorber, while measurements with NIR (785 nm) were performed in complete devices using Horiba Jobin Yvon FHR640 and iHR320 monochromators coupled with CCD detectors. The first monochromator is optimized for the UV and visible spectral ranges and was used with 442 nm (He–Cd gas laser) and 532 nm (solid state laser) excitation wavelengths. The second monochromator is optimized for the NIR range and was used with a 785 nm (solid state laser) excitation wavelength. The power density of the lasers was kept below 150 W cm<sup>2</sup> and the spot size was ~70 <span class="math-tex">\(\mu\)</span>m. The measurements were performed in a backscattering configuration through a specific probe designed at IREC. The J–V characteristics of the devices were obtained under simulated AM1.5 illumination (1000 W m2 intensity at room temperature) using a pre-calibrated Class AAA solar simulator (Abet Technologies Sun 3000).</p>
A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening [Feature Tables]
<p>This repository is supplementary to the manuscript "High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening" (DOI: 10.1021/acs.est.1c04168) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.</p> <p>Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>) for the annotated features. </p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Data, scripts and model output to perform spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea
<p>This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019). </p>
Synthetic geospatial data for performance analysis of geospatial database systems
<p>This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosaptial datasbases. </p> <p>The datasets is composed of four json file, characterized by different size. They can be used to analyze the scalability of geospatial datasets with respect to the database size.</p> <p>Each json file contains a set of "points", each one characterized by a set of random attributes (description, url of a picture linked to the point, creation date, delete date, update date, identifier, partition identifier).</p> <p>The synthetically generated points are uniformly distributed among the world.</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Coolpup.py – a versatile tool to perform pile-up analysis of Hi-C data
<p>Data used for the analysis and to generate the figures. After un-tar-ing, the data is present in three folders. /coolers contains Hi-C data in the .cool format, and associated text files. /beds contains .bed and .bedpe files used in the analysis. /enrichment_jsons contains .json files with results of the "loop-ability" analysis. Code for the data analysis is available here: https://github.com/Phlya/coolpuppy_paper</p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Performance measurements for in-depth energy analysis of security algorithms and protocols for the Internet of Things
<p>Performance dataset of cryptographic algorithms running on the following embedded devices (results in ms):</p> <p><strong>nuc </strong>The NUCLEO-L073RZ is a STM32 Nucleo-64 Development Board of STMicroelectronics. It features the STM32L073RZT6 32~MHz ARM Cortex-M0+ microcontroller with 192~KB flash memory and 20~KB RAM.<br> <strong>msp </strong>The TI SimpleLink MSP-EXP432P401R development kit uses the MSP432P401R 48~MHz ARM Cortex-M4F microcontroller with 256~KB flash and 64~KB RAM.<br> <strong>max </strong>The MAXREFDES\#100 health sensor platform features the MAX32620 96~MHz ARM Cortex-M4F microcontroller with 2~MB flash and 256~KB RAM. It has a wide range of sensors, like a human body temperature sensor and a heart rate sensor.</p> <p>The measured cryptographic operations:</p> <ul> <li><strong>The basic arithmetic operations for elliptic curve cryptography </strong>(point addition~(PA), point doubling~(PD), point multiplication~(PM), and fixed-point multiplication~(PMG))</li> <li><strong>The AES symmetric-key cipher in five modes of operations</strong> (Electronic Codebook (ECB), Cipher Block Chaining (CBC), Counter (CTR), Counter with CBC-MAC (CCM), and Galois/Counter Mode (GCM))</li> <li><strong>Hash functions </strong>(SHA256 and SHA3-256)</li> </ul> <p>The performance of all identified basic operations is measured on the three platforms. 50 time measurements are done for each basic operation using the platforms' available timer. Moreover, the AES cipher operation is an encryption on 256 Bytes of data. We have chosen a multiple of the AES block size, because, longer time periods ensure less influence of potential timing inaccuracies like an early start and late end. For the hash function, the maximum input size of the respective algorithm for one round is chosen as follows: 55~B for SHA256 and 135~B for SHA3-256. The total available internal state size is not used for SHA256 and SHA3-256, as we take into account the minimal padding or suffix that is required for the last block of input data. Note that the most optimal scenario, i.e. the maximum amount of input data to fill up the internal state completely, is used for each of the operations.</p> <p>All basic operations are implemented using software libraries and cross-compiled with the GNU Tools for ARM Embedded Processors version 6-2017-q2-update. Furthermore, the compiler is configured to optimise for size (-Os). The RELIC-toolkit library is used to implement the EC arithmetic and the SHA256 hash function. We use the SECG K-256 prime elliptic curve, BASIC;COMBA;COMBA;MONTY;MONTY;SLIDE configuration for the prime field arithmetic, and PROJC;LWNAF;COMBS;INTER}} configuration for the prime elliptic curve arithmetic. For more information on how to configure RELIC and other examples that use it, we refer to the relic-toolkit wiki. The AES ciphers are implemented using Mbed TLS and SHA3 using wolfCrypt. We use the SHA3-256 hash function as specified in FIPS PUB 202.</p>
Supporting dataset for: "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering"
<p>This dataset was used in the meta-analysis and hierarchical clustering published in "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering" in the Journal of Dairy Science. We searched Web of Science and Google Scholar databases through March 2020 with the terms “plasma EAA,” “milk urea” or “blood urea,” and “dairy” or lactating dairy”. To be included in our study, the papers must have met the following selection criteria: (1) been published in English in a peer-reviewed journal; (2) reported dietary ingredients on a DM basis and at minimum dietary CP concentration; (3) used treatments based on diet changes (e.g., no infusion trials were included); (4) reported DMI, lactation performance, and milk components yield; (5) reported all individual [EAA]p (excluding Trp); and (6) reported blood urea-N or plasma urea-N. Infusion studies were excluded to avoid possible effects of method of EAA supply (e.g., infusion vs. feeding) and to narrow the scope of application. The final dataset included 22 studies and 96 dietary treatments. For a more complete description of the methods, please refer to the published paper. </p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
<p>The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming.</p> <p>Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This <i>Bee Tracker</i> software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax as well as the IDs of their nests according to their positions in the nesting unit.</p> <p>The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters.</p> <p>The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.</p>
Coverage and Performance Analysis of 5G Non-Standalone Deployments
<p>Passive and active network measurements used for analysing 'Coverage and Performance Analysis of 5G Non-Standalone Deployments'</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Performance of Advanced Ambu Bag System among Adult Patients with Mechanical Ventilation: A Mixed-Effects Analysis
<p>We conducted the study at the Department of Stroke Care of the Can Tho Central General Hospital, Vietnam. There are eight intensive care beds for critical illness. The study was performed according to the Helsinki Declaration and approved by the Can Tho Central General Hospital ethics committee. All patients gave written informed consent by a legal surrogate. We enrolled patients with mechanical ventilation between November 2022 and September 2023. The inclusion criteria were: (1) patients aged 16 years and older, (2) pulse rate less than 120 times per minute, (3) systolic blood pressure from 110 to 160 mmHg, (4) peripheral oxygen saturation (SpO<sub>2</sub>) greater than 90%, (5) spontaneous breathing rate less than 28 times per minute, (6) end-tidal carbon dioxide (EtCO<sub>2</sub>) from 20 to 45 mmHg, (7) secretion required suction less than one time per hour, (8) positive end-expiratory pressure less than or equal to 5 cmH<sub>2</sub>O, (9) fraction of inspired oxygen less than or equal to 60%, (10) minute ventilation less than 15 liters per minute, (11) diameter of a tracheal or tracheostomy tube greater than or equal to 7.0 mm, (12) no usage of sedation, (13) normal ST wave in the electrocardiogram. Patients were excluded from the trial if they had one of the following conditions: (1) acute myocardial infarction, (2) acute pulmonary embolism, or (3) new dangerous arrhythmias appeared in this episode (multiform ventricular ectopy, bigeminy or trigeminy ventricular ectopy, coupled ventricular ectopy, R-on-T ventricular ectopy, high-grade atrioventricular heart block, supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, ventricular fibrillation), (4) using vasopressors or inotropic agents. Patients could withdraw from the study at any time without giving any reason. Besides, the patient stopped the trial of the advanced Ambu bag system immediately when one of the signs appeared, such as (1) the peripheral oxygen saturation lower than 90% prolonging more than 1 minute, (2) the end-tidal carbon dioxide greater than 45 mmHg or less than 15 mmHg prolonging more than 10 minutes, (3) pulse rate greater than 120 times per minute or less than 60 times per minute prolonging more than 10 minutes, (4) systolic blood pressure greater than 170 mmHg prolonging more than 10 minutes, (5) appearing dangerous arrhythmias, (6) progressive cognitive impairment (based on Grady coma scale), or (7) any abnormal sign that the physician evaluated the patient required respiratory support immediately with conventional mechanical ventilation.</p> <p> The following is the meaning of the variables in the study:</p> <p>age: Age of study participants.</p> <p>gender: Gender of study participants (0: Woman, 1: Man).</p> <p>day1: Day of admission to the hospital</p> <p>day2: Intervention day.</p> <p>dia1: Major disease.</p> <p>dia2: Cause of respiratory failure.</p> <p>nihss1: National Institute of Health Stroke Scale on admission</p> <p>hsg: Severity of cerebral hemorrhage (0: No hemorrhagic stroke, hi1: Scattered small petechiae, no mass effect, hi2: Confluent petechiae, no mass effect, ph1: Hematoma within infarcted tissue, occupying <30%, no substantive mass effect, ph2: Hematoma occupying 30% or more of the infarcted tissue, with obvious mass effect, 3a: Parenchymal hematoma remote from infarcted brain tissue, 3b: Intraventricular hemorrhage, 3c: Subarachnoid hemorrhage, 3d: Subdural hemorrhage)</p> <p>aspects1: Alberta stroke program early CT score of anterior circulation on CTscan</p> <p>aspect2: Alberta stroke program early CT score of anterior circulation on DWI- Diffusion-weighted Imaging.</p> <p>aspects3: Alberta stroke program early CT score of posterior circulation on CTscan</p> <p>aspects4: Alberta stroke program early CT score of posterior circulation on DWI- Diffusion-weighted Imaging.</p> <p> </p> <p>nihss2: National Institute of Health Stroke Scale before intervention</p> <p>grady: Grady coma scale before intervention</p> <p> </p> <p>rtpa: Use alteplase (0: No, 1: Yes)</p> <p>thromb: Thrombectomy (0: No, 1: Yes)</p> <p>crani: Craniectomy (0: No, 1: Yes).</p> <p>coil: Endovascular coiling (0: No, 1: Yes)</p> <p> </p> <p>mode: Ventilation mode</p> <p>mv: Mechanical ventilation (L/min)</p> <p>fio2: Fraction of inspired oxygen (%)</p> <p>peep: Positive end-expiratory pressure (cmH<sub>2</sub>O)</p> <p>sc: Static compliance (mL/cmH<sub>2</sub>O)</p> <p>alv: Pulmonary consolidation (0: No, 1: ¼ lung, 2: ½ lung, 3: ¾ lung, 4: Complete lung)</p> <p>sf: spo2/fio2 ratio.</p> <p> </p> <p>p13a: Number of pulse beats at time -13 (conventional mechanical ventilation stage)</p> <p>s13a: Systolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>d13a: Diastolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>sp13a: SpO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p>e13a: EtCO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p> </p> <p>p12a: Number of pulse beats at time -12 (conventional mechanical ventilation stage)</p> <p>s12a: Systolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>d12a: Diastolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>sp12a: SpO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p>e12a: EtCO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p> </p> <p>p11a: Number of pulse beats at time -11 (conventional mechanical ventilation stage)</p> <p>s11a: Systolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>d11a: Diastolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>sp11a: SpO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p>e11a: EtCO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p> </p> <p>p10a: Number of pulse beats at time -10 (conventional mechanical ventilation stage)</p> <p>s10a: Systolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>d10a: Diastolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>sp10a: SpO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p>e10a: EtCO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p> </p> <p>p9a: Number of pulse beats at time -9 (conventional mechanical ventilation stage)</p> <p>s9a: Systolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>d9a: Diastolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>sp9a: SpO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p>e9a: EtCO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p> </p> <p>p8a: Number of pulse beats at time -8 (conventional mechanical ventilation stage)</p> <p>s8a: Systolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>d8a: Diastolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>sp8a: SpO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p>e8a: EtCO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p> </p> <p>p7a: Number of pulse beats at time -7 (conventional mechanical ventilation stage)</p> <p>s7a: Systolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>d7a: Diastolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>sp7a: SpO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p>e7a: EtCO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p> </p> <p>p6a: Number of pulse beats at time -6 (conventional mechanical ventilation stage)</p> <p>s6a: Systolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>d6a: Diastolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>sp6a: SpO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p>e6a: EtCO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p> </p> <p>p5a: Number of pulse beats at time -5 (conventional mechanical ventilation stage)</p> <p>s5a: Systolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>d5a: Diastolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>sp5a: SpO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p>e5a: EtCO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p> </p> <p>p4a: Number of pulse beats at time -4 (conventional mechanical ventilation stage)</p> <p>s4a: Systolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>d4a: Diastolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>sp4a: SpO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p>e4a: EtCO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p> </p> <p>p3a: Number of pulse beats at time -3 (conventional mechanical ventilation stage)</p> <p>s3a: Systolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>d3a: Diastolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>sp3a: SpO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p>e3a: EtCO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p> </p> <p>p2a: Number of pulse beats at time -2 (conventional mechanical ventilation stage)</p> <p>s2a: Systolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>d2a: Diastolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>sp2a: SpO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p>e2a: EtCO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1a: Number of pulse beats at time -1 (conventional mechanical ventilation stage)</p> <p>s1a: Systolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>d1a: Diastolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>sp1a: SpO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p>e1a: EtCO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p> </p> <p>p0a: Number of pulse beats at time 0 (conventional mechanical ventilation stage)</p> <p>s0a: Systolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>d0a: Diastolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>sp0a: SpO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p>e0a: EtCO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1b: Number of pulse beats at time +1 (advanced Ambu bag system stage)</p> <p>s1b: Systolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>d1b: Diastolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>sp1b: SpO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p>e1b: EtCO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p> </p> <p>p2b: Number of pulse beats at time +2 (advanced Ambu bag system stage)</p> <p>s2b: Systolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>d2b: Diastolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>sp2b: SpO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p>e2b: EtCO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p> </p> <p>p3b: Number of pulse beats at time +3 (advanced Ambu bag system stage)</p> <p>s3b: Systolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>d3b: Diastolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>sp3b: SpO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p>e3b: EtCO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p> </p> <p>p4b: Number of pulse beats at time +4 (advanced Ambu bag system stage)</p> <p>s4b: Systolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>d4b: Diastolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>sp4b: SpO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p>e4b: EtCO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p> </p> <p>p5b: Number of pulse beats at time +5 (advanced Ambu bag system stage)</p> <p>s5b: Systolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>d5b: Diastolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>sp5b: SpO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p>e5b: EtCO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p> </p> <p>p6b: Number of pulse beats at time +6 (advanced Ambu bag system stage)</p> <p>s6b: Systolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>d6b: Diastolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>sp6b: SpO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p>e6b: EtCO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p> </p> <p>p7b: Number of pulse beats at time +7 (advanced Ambu bag system stage)</p> <p>s7b: Systolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>d7b: Diastolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>sp7b: SpO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p>e7b: EtCO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p> </p> <p>p8b: Number of pulse beats at time +8 (advanced Ambu bag system stage)</p> <p>s8b: Systolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>d8b: Diastolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>sp8b: SpO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p>e8b: EtCO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p> </p> <p>p9b: Number of pulse beats at time +9 (advanced Ambu bag system stage)</p> <p>s9b: Systolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>d9b: Diastolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>sp9b: SpO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p>e9b: EtCO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p> </p> <p>p10b: Number of pulse beats at time +10 (advanced Ambu bag system stage)</p> <p>s10b: Systolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>d10b: Diastolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>sp10b: SpO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p>e10b: EtCO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p> </p> <p>p11b: Number of pulse beats at time +11 (advanced Ambu bag system stage)</p> <p>s11b: Systolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>d11b: Diastolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>sp11b: SpO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p>e11b: EtCO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p> </p> <p>p12b: Number of pulse beats at time +12 (advanced Ambu bag system stage)</p> <p>s12b: Systolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>d12b: Diastolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>sp12b: SpO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p>e12b: EtCO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p> </p> <p>p13b: Number of pulse beats at time +13 (advanced Ambu bag system stage)</p> <p>s13b: Systolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>d13b: Diastolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>sp13b: SpO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p>e13b: EtCO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p> </p> <p>p14b: Number of pulse beats at time +14 (advanced Ambu bag system stage)</p> <p>s14b: Systolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>d14b: Diastolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>sp14b: SpO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p> <p>e14b: EtCO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p>
Impacts of centralized control on mixed traffic network performance: A strategic games analysis
<p>This dataset contains the data that were used to assess the proposed framework within the context of the case study in the paper entitled "Impacts of centralized control on mdaixed traffic network per-formance: A strategic games analysis".</p>
Computational Artifacts for the Paper "Are Noise-resilient Logical Timers useful for Performance Analysis?"
<p>This repository contains computational artifacts for the paper "Are Noise-resilient Logical Timers useful for Performance Analysis?" to be submitted to <a href="https://sc-protools-workshop.github.io/protools24/">ProTools@SC24.</a></p> <p>See also the <a href="https://sc24.supercomputing.org/program/papers/reproducibility-initiative/">SC24 reproducibility initiative.</a></p> <p> </p> <p>Contains</p> <ul> <li>Source code of <a href="https://doi.org/10.5281/zenodo.10822140">Score-P </a>, including implementation of the logical clock algorithm from the paper</li> <li>Software to post-process the Cube files generated by measurements</li> <li>Benchmarks <ul> <li>Source code</li> <li>Configuration skripts</li> <li>Measurement results, including output logs, Cube files</li> <li>Post-processing skripts and results</li> </ul> </li> </ul> <p> </p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation
<p>The algorithm has two stages, first is pre-processing of given MRI image and after that<br> segmentation and then perform morphological operations.</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.