![]() As such, the objective of the present paper is to present an empirical investigation on the relationship between digitalization and happiness in the European Union (EU) during the period 2019–2021, before and during the COVID-19 pandemic. In this context, empirical research on digitalization has grown enormously during the last decade however, studies on the relationship between digitalization and happiness remain limited. Over the years, technology, especially digitalization, has revolutionized the world and changed our lives. In our daily life, happiness is conditioned by different variables, such as relationships with certain groups of individuals, health, security values, expectations, etc. One of the most important goals of humanity has always been happiness. Conclusions: The results of our analyses confirmed that a higher statistically significant positive relationship was identified between the digital performance of the EU-27 countries and their innovation performance evaluated using the SII versus the innovation performance using the GII. We consider Model 2 to be the most suitable model, which is the result of the Fixed Effects Model (FEM). ![]() Results: To select the resulting model from the 3 proposed models (Model 1 (OLS), Model 2 (FEM), and Model 3 (REM)), test criteria such as F-test, Breusch-Pagan test, and Hausman test were used. The length of the analyzed period was 5 years (2016-2020). Sample: We have chosen the EU-27 countries for our analyses. Methods: To verify the hypotheses, we used the Kendall Tau coefficient (τ), panel data regression analysis and through this analysis, we created 3 models. The main aim was fulfilled by 2 partial goals and subsequently set 2 hypotheses. Aims: The main aim was to investigate the interrelationships between the digital and innovation performance of the EU countries using the selected global indices (DESI, GII, SII). The results revealed that the classification improved from 92.52 to 100%.KeywordsDigitalizationEconomic growthPrincipal component analysisNeural networksDeep learningīackground: The global digital economy is developing quickly, and innovation plays a crucial role in today's economic growth. We train a 2-layer neural network on the score matrix given by the three retained principal components. Then, we applied principal component analysis and reduced the original dataset to 3 principal components which retain together 78.21% of the initial variability. We selected 15 indicators on which we first trained a 2-layer neural network and we obtained a classifier with 92.52% accuracy. The used databases were Eurostat and World Bank. In this paper, we use deep learning and principal component analysis as an efficient technique to improve the accuracy of classification for the set of EU countries classified according to The Digital Economy and Society Index. Our research aimed to identify and analyze the correlations between digitization and economic growth of EU countries in the period 2019–2021. I think the biggest hurdle will be figuring out an unambiguous specification.From an economic point of view, shortly digitalization will lead not only to progressive growth, but also to important transformations of jobs and to the reorganization of the way of carrying out the activity of retail, transport, and banking services. It looks like we don't support modifying the resolution of non-standard spaces. If you don't do some padding on your slices, motion correction is going to place some values outside the image bounds.Īnd whether it is possible to resample the BOLD so that the resolution matches in the T1 within fmriprep? The space-func outputs should be in your original space, although with limited FoV that seems like a bad idea. If you're interested in the details, it's defined here: The idea is not to upsample data needlessly, but we did still need a common target to resample each BOLD series to, to ensure alignment. ![]() To resample to T1w, the T1w is down-sampled to the BOLD resolution, and that is used as the resampling target. I just wondered why the dimensions where different between the preproc and the original BOLD, The pre-processed data matches neither of these data:įslinfo sub-0014_ses-1_task-rest_acq-TR2200_space-T1w_desc-preproc_ The original T1 has the following dimensions: ![]() The original bold has the following dimensions:įslinfo sub-0014_ses-1_task-rest_acq-TR2200_bold.nii I am running this command as I want my pre-processed data to be registered to my T1 before I put the data through Melodic:įmriprep-docker /media/HDD/BIDS/Nifti/ /media/HDD/BIDS/derivatives/ participant -participant-label 0062 -output-spaces fsnative T1w func I wonder if anyone can let me know why the dimensions of my pre-processed functional data do not match any of the input spaces. ![]()
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