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33 results for “Technology Network”
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Machine Learning Enabled Multi-Radio Access Technology Selection in 5G Networks
<p>In this paper, we present a machine learning algorithm for effective RAT selection in 5G networks by considering the geo-location (latitude and longitude) of the user as well as the received signal strength intensity (RSSI) from the base station as basic parameters, real live data from a 5G network base-station were collated, divided into training and testing data-sets, the training data-sets (input) were used to train models of supervised machine learning classification algorithm: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost); these trained models are further tested with input test data-sets to predict/select the appropriate RAT (4G/5G) as labelled output. Evaluation of results showed a measure of accuracy of our chosen model of RAT selection; (XGBoost) at optimal level 93.86\%, which was further cross validated at 92.9\% when compared with other algorithms for its effectiveness on future data and mitigation ability on over-fitting and under-fitting issues, hence recommended for planning and optimization purposes in similar urban/dense-urban environment to assist in maintaining the rapidly increasing demand of network connections and devices.</p>
Dataset: First Trust S-Network Future Vehicles & Technology ETF (CARZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
<p>In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency. </p>
Public Procurement: a systemic approach to sustainability and technology – SAPIENS Network Webinar Series
<p>On the 5th of June 2024, the SAPIENS Network Early Stage Researchers <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sap-in-construction/">Alexandru Buftic</a> (Babeș-Bolyai University), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/delivering-sap-through-collaboration/">Felippe Vilaça</a> (University of Gävle), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/measuring-lcc/">Haitham Ghaida</a> (Hasselt University) and <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/industry-4-for-sp/">Nadia Sava</a> (Babeș-Bolyai University) delivered a scientific webinar on the topic of <em>Public Procurement: a systemic approach to sustainability and technology</em>.</p> <p>The webinar was moderated by <a href="https://sapiensnetwork.eu/dacian-dragos/">Dacian C. Dragoș</a>, Professor of Administrative and European Law with the Public Administration and Management Department at Babes-Bolyai University, <a href="https://sapiensnetwork.eu/sebastien-lizin/">Sebastien Lizin</a>, Assistant Professor at the Faculty of Business Economics and vice-chair of the Department of Economics at Hasselt University and <a href="https://www.linkedin.com/in/roxana-vornicu-phd-49470347/">Roxana Vornicu</a>, PhD, Senior Lecturer at King´s College London, Centre of Construction Law, and Managing Partner of Sirbu & Vornicu Law.</p> <p>The webinar tackled various current challenges related to sustainability in public procurement. The presentations covered different topics, united by the concepts of sustainability and digitalisation in public procurement. The speakers highlighed the value of taking a systemic approach to sustainability in public procurement, the role of monitoring green and sustainable considerations in the process, the importance of EU legislation in the uptake of sustainability as well as effect of life cycle assessment reserch for sustainable building design. </p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
TWEETHER Future Generation W-band Backhaul and Access Network Technology
<p>Paper Presented at EUCNC 2017. Data of lens antenna simulated by 3D simulator and measured by Vector Network analyser. Measurements of chips on wafer.</p>
Wind Technology Network data
<p>This dataset contains nodes and edges of the wind technology diffusion network inferred in the paper:</p> <p> </p> <p>S. Halleck-Vega, A. Mandel & K. Millock, (2018). "Accelerating diffusion of climate-friendly technologies: A network perspective." Ecological Economics, Vol.152, pp 235-245.</p>
Data from: Research on potential disruptive technology identification based on technology network
<p><span>Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, the paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the LDA topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil UAVs as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the main equipment, data acquisition, and information transmission.</span></p>
Network Theme: Sensor Technologies - Dr Sergiy Korposh (University of Nottingham)
<p>This video is the second talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Sensor Technologies - Dr Sergiy Korposh (University of Nottingham)</p> <p>Bio: Dr Sergiy Korposh is an Associate Professor in Electronics, Nanoscale Bioelectronics and Biophotonics at University of Nottingham. His current research focuses on the development of fibre optic sensors and instrumentation for biomedical application from discovery at the interface with physics and chemistry through to application addressing major healthcare challenges. He has published over 100 (h-index 21) peer-reviewed journal and conference papers, book contributions, holds 11 patents (4 licensed to UK and Japanese companies) and given invited presentations at international meetings in the field of optical fibre chemical sensors. He has managed as a PI and Co-I a total funding portfolio of £3.5 million in the area of biomedical point of care sensors. He was a Director of the EPSRC funded Network Cyclops (EP/N026985/1, Closed Loop Control Systems for Optimising Treatment, http://www.healthcaretechnologies.ac.uk/cyclops/); with the aim to facilitate the formation of a community of academics, clinicians and industrialists, across multiple disciplines (photonic sensing, advanced materials, treatment, and mathematical modelling), including international collaborators to address grand challenges in automation of treatment in healthcare.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/tv0JydXOdUI</p>
Dataset: Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies
<p>Dataset for the following paper: <a href="https://arxiv.org/abs/2405.04401">"Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies", Julien Baglio, arXiv:2405.04401</a></p> <p>It contains:</p> <ul> <li>one folder named "data_for_all_plots" containing the raw data for the s, t, and y distributions for all the figures of the paper as well as a Jupyter notebook to generate the figures.</li> <li>one file named "variance_calculations_qGAN.txt" containing the data to calculate the errors for the KL divergences.</li> </ul>
Project for Reproductive Equity Through Volunteers and Entrepreneurship, Networks and Technology
ClinicalTrials.gov study NCT03995043. IPD Sharing: NO. Countries: 1. Publications: 12.
Using Networks, Informatics, Technology, and Education in Care for People With Diabetes
ClinicalTrials.gov study NCT00421850. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A New Neuroregulatory Technology for the Therapy of AN Based on the Pathological Neural Network of ACC
ClinicalTrials.gov study NCT06152640. IPD Sharing: YES. Countries: 1. Publications: 9.
Data from: Technology networks: the autocatalytic origins of innovation
Open the record for dataset details and reuse information.
Data from: Research on potential disruptive technology identification based on technology network
Open the record for dataset details and reuse information.
MODELING OF TELECOMMUNICATION NETWORKS BASED ON FUZZY LOGIC TECHNOLOGY
Open the record for dataset details and reuse information.
Research on key generic technology prediction based on graph neural networks under the perspective of patent citation - An example from the field of genetic engineering
<p>In this research, we adopted graph neural network models for key generic prediction based on cited patent data. Through the construction of the patent citation network and the design of a key generic evaluation system, 20879 relevant patents and 51,610 irrelevant patents were screened out. Further, we utilized the LDA topic model to interpret technical topics at a finer granularity. Finally, to test the effectiveness of this method, we took the field of genetic engineering as an example for key generic technology prediction, with an accuracy rate of 95%.</p>
Opinion of ethics committee representatives on the use of social networks and new technologies in clinical trials. Nominal group
<p>Nominal group database to find out the opinion of ethics committee representatives on the use of social networks and new technologies in clinical trials.</p>
Cognitive/Physical Computer-Game Blended Training With Personalized Brain Network Activation Technology for the Elderly
ClinicalTrials.gov study NCT02417558. IPD Sharing: Not stated. Countries: 0. Publications: 5.
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.