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ShareScore release 0.9.0
Dataset results
9 results for “Synergy Model”
Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling
<p>The datasets contain input data and data emulated by Neural networks (NN) used in the study 'Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling'</p> <p>- <strong>NN2018_CMEMS_ARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) along ARGO float paths in 2018 with vertical resolution taken from CMEMS (13 depth levels in the depth range studied here); NN was applied either on CMEMS physics ('C') or on ARGO physics ('A'); additionally, CMEMS BGC model data (Chlorophyll-a and Oxygen) along these paths are included; (filenames follow the names of floats given in Table 1:<em> floatname</em>_2018_NNARGOCMEMS.mat)</p> <p>- <strong>NNalongARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700 nm) along ARGO float paths together with input ARGO data (time, latitude, longitude, salinity, temperature, sigma_T and BGC variables); all variables are mapped with 1m vertical resolution; depth range is [1m 150m] ; additionally, float ogs7 data include NO3, float hzg1 data does not contain Chlorophyll-a and backscatter at 700 nm; (filenames follow the names of floats given in Table 1:<em> floatname</em>_euph_1mRes_150mALLINCLNN.mat)</p> <p>- <strong>NNReconBlackSea.tar.gz</strong>: archive contains Matlab binary files consisting of basin wide NN derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) for the years 2015-19 and 2010/11 (weekly mean data); additionally CMEMS data (time, latitude, longitude, salinity, temperature, sea surface height) are provided for the photic zone; (filenames are reconNNCMEMS_2015_2019.mat and reconNNCMEMS_2010_2011.mat, respectively)</p>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Pre-calculated SHAP Values for Drug Synergy Model
<p>Pre-calculated SHAP Values used to evaluate feature importance in the drug synergy model.</p>
Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma (DDLS) part 2
GEO Series GSE50750. Homo sapiens. 2 samples. Type: Genome variation profiling by genome tiling array.
Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma (DDLS)
GEO Series GSE50751. Homo sapiens. 8 samples. Type: Expression profiling by array; Genome variation profiling by genome tiling array.
Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma (DDLS) part 1
GEO Series GSE50749. Homo sapiens. 6 samples. Type: Expression profiling by array.
Does the Synergy Model Implementation Improve the Transition from In-Hospital to Primary Care? The Experience from an Italian Cardiac Surgery Unit, Perspectives, and Future Implications
<p>Dellafiore F, Caruso R, Nania T, Pittella F, Fiorini T, Caruso MP, Zaffino G, Stievano A, Arrigoni C. Does the Synergy Model Implementation Improve the Transition from In-Hospital to Primary Care? The Experience from an Italian Cardiac Surgery Unit, Perspectives, and Future Implications. Int J Environ Res Public Health. 2022 May 5;19(9):5624. doi: 10.3390/ijerph19095624. PMID: 35565012; PMCID: PMC9105911.</p> <p>Abstract</p> <p>The demand for care services in the healthcare system has changed and is triggering a smooth transition from in-hospital to primary care. In this regard, patient-centered-care models of care delivery might provide a framework to follow patients' journeys throughout their transition between different levels of care. Accordingly, an Italian research group at a cardiac hospital in Northern Italy implemented the Synergy Model in a Cardiac Surgery Unit, a patient-centered-care model, and is using the framework of the model to guide a smooth transition of patients towards rehabilitation and primary care after their hospitalization. This discursive paper is focused on the experience, perspectives, and future implications of adopting the Synergy Model to facilitate the transition from in-hospital to primary care. The presented experience and discussion might be helpful to the international debate regarding the strategies to boost a smooth transition from in-hospital to primary care.</p>
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OpenNeuro
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