FIG. 14.1 Number of papers per country. The index number can be assigned to the year of publication, see Table 14.1 . Meaning in the figure: Country name abbreviation (number of papers in the country) [number of the paper from Table 14.1 ]. More about this image found in Number of papers per country. The index number can be assigned to the year ...

FIG. 1.1 Number of journal articles retrieved from the Web of science in 5-year periods with “technology” in the title. More about this image found in Number of journal articles retrieved from the Web of science in 5-year peri...

FIG. 1.2 Number of articles by year for the keyword “technology integration.” More about this image found in Number of articles by year for the keyword “technology integration.”

FIG. 1.4 Number of indexed publications in searched databases. More about this image found in Number of indexed publications in searched databases.

FIG. 16.2 Number of papers focused on physics textbooks CA in years. More about this image found in Number of papers focused on physics textbooks CA in years.

FIG. 16.4 The number of publications published in each decade according to the level of education. More about this image found in The number of publications published in each decade according to the level ...

FIG. 16.6 Number of papers focused on physics textbooks in each country. More about this image found in Number of papers focused on physics textbooks in each country.

FIG. 3.12 Influence of mass flow rate m_IL, a number of theoretical stages N_t (a), absorber temperature T₁ (b), flash drum temperature T₂ (c), and flash drum pressure P₂ (d) on the water content y₁ in the gas product. Reprinted with permission from Yu et al., Energy Fuels 31 (2), 1429–1439 (2017). Copyright 2017 American Chemical Society. More about this image found in Influence of mass flow rate m_IL, a number of th...

FIG. 11.6 The sensitivity of Kappa number with varying temperature and PSA weight fraction. More about this image found in The sensitivity of Kappa number with varying temperature and PSA weight fra...

FIG. 11.12 The Kappa number of the wood fibers reaching the set-point. More about this image found in The Kappa number of the wood fibers reaching the set-point.

FIG. 10.9 Prediction of inter-cyclic and intra-cyclic battery dynamics using OASIS as a two-timescale modeling approach (k = 1, …, m and s = 1, …, n denote operating cycle number and sampling time, respectively). More about this image found in Prediction of inter-cyclic and intra-cyclic battery dynamics using OASIS as...

FIG. 24.1 Publications illustrating the state of research between 2015–2020 situated in relation to previous studies. For each of the seven themes structuring the review, one example of the oldest and newest studies as well as the number of selected studies are given (some studies are referenced i... More about this image found in Publications illustrating the state of research between 2015–2020 situated ...

FIG. 3.3 The number of publications on cyber-physical systems and Energy 4.0 from 2013 to 2022. More about this image found in The number of publications on cyber-physical systems and Energy 4.0 from 20...

FIG. 10.2 (a) tlCA timescale (at tICA lag time = 0.2 ps) against the number of features. (b) Box and whisker plots for GMRQ test scores with different MSM hyperparameters. More about this image found in (a) tlCA timescale (at tICA lag time = 0.2 ps) against the number of featur...

FIG. 2.3 Results of the path analysis part of SEM models 1–4 show how the relationship between gender and physics identity is mediated through self-efficacy, interest, and perceived recognition (P.R.). (a) In model 1, there are only covariances between each pair of constructs: self-efficacy, P.R., and interest. (b) In model 2, self-efficacy predicts interest and P.R., and interest predicts P.R. (c) In model 3, interest predicts self-efficacy and P.R., and self-efficacy predicts P.R. (d) In model 4, P.R. predicts interest and self-efficacy, and self-efficacy predicts interest. The dashed lines represent residual covariances between constructs. The solid lines represent regression paths, and the numbers on the lines are standardized regression coefficients (β values), which represent the strength of the regression relations. Each regression line thickness qualitatively corresponds to the magnitude of the β value. Regression coefficients with p < 0.050 are indicated by superscript ***, p < 0.010 are indicated by superscript **, and p ≤ 0.001 are indicated by no superscript. For clarity, we have removed the statistically insignificant regression paths from gender to identity or gender to P.R. and self-efficacy in models 2 and 3. More about this image found in Results of the path analysis part of SEM models 1–4 show how the relationsh...