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315 results for “Zero”
Shell biomass material supported nano-zero valent iron to remove Pb2+ and Cd2+ in water
Nanoscale zero-valent iron (NZVI) has a high adsorption capacity for heavy metals, but easily forms aggregates. Herein, preprocessed undulating venus shell (UVS) is used as support material to prevent NZVI from reuniting. The SEM and TEM results show that UVS had a porous layered structure and NZVI particles were evenly distributed on the UVS surface. A large number of adsorption sites on the surface of UVS-NZVI are confirmed by IR and XRD. UVS-NZVI is utilized for adsorption of Pb2+ and Cd2+ at pH=6 in aqueous solution, and the experimental adsorption capacities are 29.91 mg•g-1 and 38.99 mg•g-1 at optimal pH, respectively. Thermodynamic studies indicate that the adsorption of ions by UVS-NZVI is more in line with the Langmuir model when Pb2+ or Cd2+ existed alone. For the mixed solution of Pb2+ and Cd2+, only the adsorption of Pb2+ by UVS-NZVI conforms to the Langmuir model. In addition, the maximum adsorption capacities of UVS-NZVI for Pb2+ and Cd2+ are 93.01 mg•g-1 and 46.07 mg•g-1.Kinetic studies demonstrate that the determination coefficients (R2) of the pseudo first-order kinetic model for UVS-NZVI adsorption of Cd2+ and Pb2+ are higher than those of the pseudo second-order kinetic model and Elovich kinetic model. Highly efficient performance for metal removal makes UVS-NZVI show potential application to heavy metal ion adsorption.
Zero-Postulation or Null-Postulation and Abstraction
<p><strong>The best bet is to find out the most fundamental components within the system</strong> and building a theory round these. In other words, a theory that is able to describe the world in totality has <strong>to keep the number of basic postulates it depends upon to zero </strong>or near zero.</p> <p><em><strong>Zero Postulation</strong></em> gives rise to abstraction. The abstraction we are talking about here may be defined as, <em><strong>“Postulation of non-postulation” or, in other words, “A system of postulation that gives equal weights to all possible solutions inside the system and favors none of such solutions over others.”</strong></em></p>
Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning
<p>This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows:</p> <ul> <li>_id: Unique MongoDB document ID</li> <li>chirp: Base 64 encoded binary float complex I/Q data</li> <li>field: Symbol location inside a LoRa frame</li> <li>tag: Name of the device that sent the frame</li> <li>date: Time and date of reception</li> <li>fn: Frame number</li> <li>rand: Random number for sorting</li> </ul> <p><strong>How to import</strong></p> <p>Extract the tar archive. Inside the directory, run the following command to import the lora2msps database:</p> <p><em>mongorestore --gzip -d lora2msps ./lora2msps</em></p> <p>This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing:</p> <p><em>mongorestore --gzip . </em></p> <p><strong>How to use</strong></p> <p>After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.:</p> <p><em>./tf_train.py train conf/experiment_lora2msps_mlp.conf</em></p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 2)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) only on the give ENGLISH corpus. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Zero Fighter Wreckage
MITSUBISHI ZERO A6M3 wreckage now located in the Imperial War Museum, London. "Japanese single seat carrier-based fighter in service from 1940." Date: 1943 https://www.iwm.org.uk/collections/item/object/70000201 148 photos taken in September 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Longyearbyen CO2 Lab - Zero Offset VSP - DH4
<p>This report describes the Zero Offset VSP Data acquisition and processing of the well DH4, Adventdalen at Svalbard.</p>
Permeability of granular mixtures under shear - videos for zero shear rate
<p><strong>Videos of fluidisation experiments</strong></p> <p>A static granular column comprising Ballotini glass beads of diameters 250 μm, 125 μm, 90 μm and 63 μm is fluidised with an increasing air flux rate. The data set includes videos of the granular column for each size fraction.</p>
PyPSA-PL: Net-zero 2050 scenario for Poland
<p>This record contains all the scripts and data from the PyPSA-PL modelling exercise that supported the report:</p> <ul> <li>Kubiczek, P., Smoleń, M. (2023). <em>Three challenging decades. Scenario for the Polish energy transition out to 2050.</em> Instrat Policy Paper 03/2024. <a href="https://www.instrat.pl/three-challenging-decades">https://www.instrat.pl/three-challenging-decades</a></li> </ul> <p>The record structure is based on the PyPSA-PL repository <a href="https://github.com/instrat-pl/pypsa-pl">https://github.com/instrat-pl/pypsa-pl</a> (v3.0).</p>
Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization
<p>This dataset contains all the data in "Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization"</p>
Supplemental idf files used for paper "Refurbishmet Methodology to Attain Thermally Comfortable near-Zero Energy Buildings Using Customizable Solutions"
<p>These are the idf files used to study different technology alternatives offered by the Reco2st project on an idealized dormitory room located in London, UK. The files are presented "as is" and were done for EnergyPlus v8.9. Includes files that might have not made it to the final paper.</p> <p>Geometry: One zone room with one external window.<br> Materials: External construction is cavity wall, with bricks on both sides, gypsum plaster on internal side<br> Internal walls brick single layer with gypsum plaster.<br> Ceiling and floor reinforced concrete. Plaster finish on ceiling, nylon carpet on floor<br> Double glazed window. Five panes available: one large central fixed, two small clerestory opening, and two large side opening.<br> For simulation purposes a single equivalent area of all openings modelled.<br> Material data sources: gov.scot, puravent.co.uk, nature.com. SHGC from LBL.</p> <p>This is part of work done for ReCO2ST - Residential Retrofit assessment platform and demonstrations for near zero energy and CO2 emissions with optimum cost, health, comfort and environmental quality. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768576.</p>
Parametric exploration of zero-energy modes in three-terminal InSb-Al nanowire devices
<p>Files inlcude 1) Raw data for all figure 2) data process file 3) Generated figures</p>
Dataset for the paper: "Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance"
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Andres Parra-Puerto, Javier Rubio-Garcia, Matthew Markiewicz, Zhuo Zheng and Anthony Kucernak</p> <p>Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance </p> <p>DOI: https://doi.org/10.1002/celc.202101617 </p> <p>Please cite the above reference if you wish to use this data </p> <p>DOI of this data file is: 10.5281/zenodo.6261512</p>
Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
<p>This is preprocessed data and pretrained models from two of our papers:</p> <p>"Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> <a href="https://arxiv.org/abs/1910.10838">https://arxiv.org/abs/1910.10838</a></p> <p> "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) <a href="https://arxiv.org/abs/2011.04839">https://arxiv.org/abs/2011.04839</a></p> <p>This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron</p> <p>More information about the directory structure and how to use the data can be found in the READMEs on GitHub.</p>
On estimating the proportion of susceptibility with zero-inflated models
<p>Data used for demonstrating the properties of "On estimating the proportion of susceptibility with zero-inflated models". </p>
Supporting Information - Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition
<p>This file accompanies "A method for estimating thermal energy loads in Canada’s remote and northern communities" by Ian Maynard and Ahmed Abdulla.</p> <p>This supporting information contains:</p> <ul> <li>Calculations and thermal loads of 40 communities discussed in the above paper</li> <li>Heating Degree Hour (HDH) distribution calculations</li> <li>Diagnostics and dataset used for developing a model for predicting annual and monthly thermal loads using ordinary least squares regression</li> </ul>
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.